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The Neuron as a Direct Data-Driven Controller (2401.01489v1)

Published 3 Jan 2024 in q-bio.NC, cs.AI, cs.SY, and eess.SY

Abstract: In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends the current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, act as controllers, steering their environment towards a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. Utilizing the novel Direct Data-Driven Control (DD-DC) framework, we model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states and optimize control. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP) with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a novel and biologically-informed fundamental unit for constructing neural networks.

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References (89)
  1. Horace Barlow. Redundancy reduction revisited. Network: computation in neural systems, 12(3):241, 2001.
  2. Predictive coding: a fresh view of inhibition in the retina. Proceedings of the Royal Society of London. Series B. Biological Sciences, 216(1205):427–459, 1982.
  3. Joseph J Atick. Could information theory provide an ecological theory of sensory processing? Network: Computation in neural systems, 3(2):213–251, 1992.
  4. Johannes H van Hateren. Theoretical predictions of spatiotemporal receptive fields of fly lmcs, and experimental validation. Journal of Comparative Physiology A, 171:157–170, 1992.
  5. Natural image statistics and neural representation. Annual review of neuroscience, 24(1):1193–1216, 2001.
  6. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience, 2(1):79–87, 1999.
  7. Efficient coding, channel capacity, and the emergence of retinal mosaics. Advances in neural information processing systems, 35:32311–32324, 2022.
  8. Predictive information in a sensory population. Proceedings of the National Academy of Sciences, 112(22):6908–6913, 2015.
  9. Remembering the past to see the future. Annual review of vision science, 7:349–365, 2021.
  10. Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers. PLoS computational biology, 17(5):e1008965, 2021.
  11. The information bottleneck method. arXiv preprint physics/0004057, 2000.
  12. Toward a unified theory of efficient, predictive, and sparse coding. Proceedings of the National Academy of Sciences, 115(1):186–191, 2018.
  13. Spike initiation by transmembrane current: a white-noise analysis. The Journal of physiology, 260(2):279–314, 1976.
  14. Reliability of spike timing in neocortical neurons. Science, 268(5216):1503–1506, 1995.
  15. Anthony J Bell. Levels and loops: the future of artificial intelligence and neuroscience. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 354(1392):2013–2020, 1999.
  16. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS biology, 3(3):e68, 2005.
  17. Structural properties of the caenorhabditis elegans neuronal network. PLoS computational biology, 7(2):e1001066, 2011.
  18. The connectome of an insect brain. Science, 379(6636):eadd9330, 2023.
  19. Tohru Katayama et al. Subspace methods for system identification, volume 1. Springer, 2005.
  20. Feedback systems: an introduction for scientists and engineers. Princeton university press, 2021.
  21. A note on persistency of excitation. Systems & Control Letters, 54(4):325–329, 2005.
  22. Formulas for data-driven control: Stabilization, optimality, and robustness. IEEE Transactions on Automatic Control, 65(3):909–924, 2019.
  23. Data-driven control based on the behavioral approach: From theory to applications in power systems. IEEE Control Syst., 2022.
  24. Regulation of synaptic efficacy by coincidence of postsynaptic aps and epsps. Science, 275(5297):213–215, 1997.
  25. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of neuroscience, 18(24):10464–10472, 1998.
  26. A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395(6697):37–44, 1998.
  27. Causes and consequences of representational drift. Current opinion in neurobiology, 58:141–147, 2019.
  28. Heterogeneity of release probability, facilitation, and depletion at central synapses. Neuron, 18(6):995–1008, 1997.
  29. Feedback from network states generates variability in a probabilistic olfactory circuit. Cell, 161(2):215–227, 2015.
  30. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLoS biology, 3(5):e153, 2005.
  31. Representational drift in the mouse visual cortex. Current biology, 31(19):4327–4339, 2021.
  32. Representational drift in primary olfactory cortex. Nature, 594(7864):541–546, 2021.
  33. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron, 65(4):472–479, 2010.
  34. Single-trial neural dynamics are dominated by richly varied movements. Nature neuroscience, 22(10):1677–1686, 2019.
  35. The importance of accounting for movement when relating neuronal activity to sensory and cognitive processes. Journal of Neuroscience, 42(8):1375–1382, 2022.
  36. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5:115–133, 1943.
  37. Frank Rosenblatt. Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Technical report, Cornell Aeronautical Lab Inc Buffalo NY, 1961.
  38. Data-enabled predictive control: In the shallows of the deepc. In 2019 18th European Control Conference (ECC), pages 307–312. IEEE, 2019.
  39. Linear tracking mpc for nonlinear systems—part ii: The data-driven case. IEEE Transactions on Automatic Control, 67(9):4406–4421, 2022.
  40. Henk J van Waarde. Beyond persistent excitation: Online experiment design for data-driven modeling and control. IEEE Control Systems Letters, 6:319–324, 2021.
  41. Online learning of data-driven controllers for unknown switched linear systems. Automatica, 145:110519, 2022.
  42. Neuroscience-inspired online unsupervised learning algorithms: Artificial neural networks. IEEE Signal Processing Magazine, 36(6):88–96, 2019.
  43. Donald O. Hebb. The organization of behavior: A neuropsychological theory. Wiley, New York, 1949.
  44. Stability versus neuronal specialization for stdp: long-tail weight distributions solve the dilemma. PloS one, 6(10):e25339, 2011.
  45. Learning cortical hierarchies with temporal hebbian updates. Frontiers in Computational Neuroscience, 17:1136010, 2023.
  46. Spike timing–dependent plasticity: a hebbian learning rule. Annu. Rev. Neurosci., 31:25–46, 2008.
  47. Roy M Pritchard. Stabilized images on the retina. Scientific American, 204(6):72–79, 1961.
  48. Microsaccades precisely relocate gaze in a high visual acuity task. Nature neuroscience, 13(12):1549–1553, 2010.
  49. Neural coding of naturalistic motion stimuli. Network: Computation in Neural Systems, 12(3):317, 2001.
  50. Generalized leaky integrate-and-fire models classify multiple neuron types. Nature communications, 9(1):709, 2018.
  51. Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli. Elife, 6:e27670, 2017.
  52. Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J. Neurophysiol, 90(3):1598–1612, 2003.
  53. Spikes: exploring the neural code, 1999.
  54. Predicting every spike: a model for the responses of visual neurons. Neuron, 30(3):803–817, 2001.
  55. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207):995–999, 2008.
  56. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural computation, 10(7):1679–1703, 1998.
  57. Electric current flow in excitable cells. 1975.
  58. Rahul Sarpeshkar. Analog versus digital: extrapolating from electronics to neurobiology. Neural computation, 10(7):1601–1638, 1998.
  59. The metabolic cost of neural information. Nature neuroscience, 1(1):36–41, 1998.
  60. Dynamic optimization: the calculus of variations and optimal control in economics and management. courier corporation, 2012.
  61. Christof Koch. Biophysics of computation: information processing in single neurons. Oxford university press, 2004.
  62. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation, 14(11):2531–2560, 2002.
  63. Neuronal avalanches in neocortical circuits. Journal of neuroscience, 23(35):11167–11177, 2003.
  64. HS Seung. How the brain keeps the eyes still. Proc. Natl. Acad. Sci. USA, 93:13339–13344, 1996.
  65. Y Zilberter. Dendritic release of glutamate suppresses synaptic inhibition of pyramidal neurons in rat neocortex. The Journal of Physiology, 528(Pt 3):489, 2000.
  66. A role for synaptic inputs at distal dendrites: instructive signals for hippocampal long-term plasticity. Neuron, 56(5):866–879, 2007.
  67. Timing rules for synaptic plasticity matched to behavioral function. Neuron, 92(5):959–967, 2016.
  68. Experience adaptively tunes the timing rules for associative plasticity. bioRxiv, 2022. 10.1101/2022.11.28.518128. URL https://www.biorxiv.org/content/early/2022/11/29/2022.11.28.518128.
  69. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature, 466(7302):123–127, 2010.
  70. Dynamic flux tubes form reservoirs of stability in neuronal circuits. Physical Review X, 2(4):041007, 2012.
  71. Sparse and powerful cortical spikes. Current opinion in neurobiology, 20(3):306–312, 2010.
  72. Behavioural report of single neuron stimulation in somatosensory cortex. Nature, 451(7174):65–68, 2008.
  73. Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature, 427(6976):704–710, 2004.
  74. 10 natural image statistics and divisive normalization. Probabilistic models of the brain, page 203, 2002.
  75. Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13(1):51–62, 2012.
  76. Adaptive rescaling maximizes information transmission. Neuron, 26(3):695–702, 2000.
  77. Predictive coding of dynamical variables in balanced spiking networks. PLoS computational biology, 9(11):e1003258, 2013.
  78. Data-driven control of complex networks. Nature communications, 12(1):1429, 2021.
  79. Louis Lapicque. Recherches quantitatives sur l’excitation electrique des nerfs traitee comme une polarization. Journal de physiologie et de pathologie générale, 9:620–635, 1907.
  80. Reinforcement learning: An introduction. MIT press, 2018.
  81. Peter Dayan. Matters temporal. Trends in Cognitive Sciences, 6(3):105–106, 2002.
  82. Paul Cisek. Beyond the computer metaphor: Behaviour as interaction. Journal of Consciousness Studies, 6(11-12):125–142, 1999.
  83. Neural mechanisms for interacting with a world full of action choices. Annual review of neuroscience, 33:269–298, 2010.
  84. Perception as a closed-loop convergence process. elife, 5:e12830, 2016.
  85. Why internal feedback is necessary in the perception-action loop. arXiv preprint arXiv:2211.05922, 2022.
  86. DA Robinson. The use of control systems analysis in the neurophysiology of eye movements. Annual review of neuroscience, 4(1):463–503, 1981.
  87. FA Miles and SG Lisberger. Plasticity in the vestibulo-ocular reflex: a new hypothesis. Annual review of neuroscience, 4(1):273–299, 1981.
  88. Mosaic model for sensorimotor learning and control. Neural computation, 13(10):2201–2220, 2001.
  89. Blowfly yaw control via electrical stimulation of the h1 lobula plate tangential cell. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 1685–1688. IEEE, 2018.
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