Papers
Topics
Authors
Recent
Search
2000 character limit reached

Emulating insect brains for neuromorphic navigation

Published 31 Dec 2023 in cs.NE | (2401.00473v1)

Abstract: Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment. Neurobiological imaging studies have revealed that this capability emerges from a path integration mechanism implemented within the insect's brain. In the present work, we emulate this neural network on the neuromorphic mixed-signal processor BrainScaleS-2 to guide bees, virtually embodied on a digital co-processor, back to their home location after randomly exploring their environment. To realize the underlying neural integrators, we introduce single-neuron spike-based short-term memory cells with axo-axonic synapses. All entities, including environment, sensory organs, brain, actuators, and the virtual body, run autonomously on a single BrainScaleS-2 microchip. The functioning network is fine-tuned for better precision and reliability through an evolution strategy. As BrainScaleS-2 emulates neural processes 1000 times faster than biology, 4800 consecutive bee journeys distributed over 320 generations occur within only half an hour on a single neuromorphic core.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (85)
  1. Complex brain and optic lobes in an early cambrian arthropod. Nature, 490(7419):258–261, 2012.
  2. Memory use in insect visual navigation. Nature Reviews Neuroscience, 3(7):542–552, 2002.
  3. High-altitude migration of the diamondback moth Plutella xylostella to the UK: a study using radar, aerial netting, and ground trapping. Ecological Entomology, 27(6):641–650, 2002.
  4. The brain of a nocturnal migratory insect, the australian bogong moth. Journal of Comparative Neurology, 528(11):1942–1963, 2020.
  5. Karl Von Frisch. The dance language and orientation of bees. Harvard University Press, 1967.
  6. Can bees see at a glance? Journal of Experimental Biology, 217(11):1933–1939, 2014.
  7. Malcolm Burrows. The neurobiology of an insect brain. Oxford University Press on Demand, 1996.
  8. Cognitive architecture of a mini-brain: the honeybee. Trends in cognitive sciences, 5(2):62–71, 2001.
  9. Three-dimensional reconstruction of brain-wide wiring networks in drosophila at single-cell resolution. Current Biology, 21(1):1–11, 2011.
  10. A visual motion detection circuit suggested by drosophila connectomics. Nature, 500(7461):175, 2013.
  11. A connectome of a learning and memory center in the adult drosophila brain. Elife, 6:e26975, 2017.
  12. A connectome of the adult drosophila central brain. BioRxiv, 2020.
  13. An anatomically constrained model for path integration in the bee brain. Current Biology, 27(20):3069–3085, 2017.
  14. A virtual reality system to analyze neural activity and behavior in adult zebrafish. Nature Methods, 17(3):343–351, 2020.
  15. The insect central complex. Current Biology, 26(11):R453–R457, 2016.
  16. Analysis of a spatial orientation memory in drosophila. Nature, 453(7199):1244, 2008.
  17. Imaging calcium in neurons. Neuron, 73(5):862–885, 2012.
  18. Neural dynamics for landmark orientation and angular path integration. Nature, 521(7551):186–191, 2015.
  19. A neural circuit architecture for angular integration in drosophila. Nature, 546(7656):101–106, 2017.
  20. Insect inspired view based navigation exploiting temporal information. In Conference on Biomimetic and Biohybrid Systems, pages 204–216. Springer, 2020.
  21. Predictive olfactory learning in drosophila. Scientific Reports, 11(6795), 2021. 10.1038/s41598-021-85841-y.
  22. Connecting artificial brains to robots in a comprehensive simulation framework: the neurorobotics platform. Frontiers in neurorobotics, 11:2, 2017.
  23. Deploying and optimizing embodied simulations of large-scale spiking neural networks on HPC infrastructure. Frontiers in neuroinformatics, 16:34, 2022a.
  24. The neurorobotics platform robot designer: modeling morphologies for embodied learning experiments. Frontiers in Neurorobotics, 16:856727, 2022b.
  25. Learning with reinforcement prediction errors in a model of the drosophila mushroom body. Nature communications, 12(1):2569, 2021.
  26. Antbot: A six-legged walking robot able to home like desert ants in outdoor environments. Science Robotics, 4(27), 2019.
  27. Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics. Applied Intelligence, 51(9):6467–6496, 2021.
  28. Reconstruction and simulation of neocortical microcircuitry. Cell, 163(2):456–492, 2015.
  29. Extremely scalable spiking neuronal network simulation code: from laptops to exascale computers. Frontiers in neuroinformatics, 12:2, 2018.
  30. GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model. Frontiers in neuroscience, 12:941, 2018.
  31. OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans. Philosophical Transactions of the Royal Society B, 373(1758):20170382, 2018.
  32. Pose estimation and map formation with spiking neural networks: towards neuromorphic slam. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2159–2166. IEEE, 2018a.
  33. Demonstrating Advantages of Neuromorphic Computation: A Pilot Study. Frontiers in Neuroscience, 2019a. 10.3389/fnins.2019.00260.
  34. Error-driven learning for self-calibration in a neuromorphic path integration system. In Robust Artificial Intelligence for Neurorobotics (RAI-NR Workshop 2019), 2019a.
  35. Self-calibration and learning on chip: towards neuromorphic robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019b.
  36. An on-chip spiking neural network for estimation of the head pose of the icub robot. Frontiers in Neuroscience, 2020.
  37. Interactive continual learning for robots: a neuromorphic approach. In Proceedings of the International Conference on Neuromorphic Systems 2022, pages 1–10, 2022.
  38. Neuromorphic computing hardware and neural architectures for robotics. Science Robotics, 7(67):eabl8419, 2022.
  39. Biomorphic control for high-speed robotic applications. In 7th HBP Student Conference on Interdisciplinary Brain Research, pages 277–281. Frontiers Media SA, mar 2023.
  40. The spinnaker project. Proceedings of the IEEE, 102(5):652–665, 2014.
  41. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197):668–673, 2014.
  42. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1):82–99, 2018.
  43. A 0.086-mm22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT 12.7-pj/sop 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm cmos. IEEE transactions on biomedical circuits and systems, 13(1):145–158, 2018.
  44. Morphic: A 65-nm 738k-synapse/mm22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT quad-core binary-weight digital neuromorphic processor with stochastic spike-driven online learning. IEEE Transactions on Biomedical Circuits and Systems, 13(5):999–1010, 2019.
  45. Efficient neuromorphic signal processing with loihi 2. In 2021 IEEE Workshop on Signal Processing Systems (SiPS), pages 254–259. IEEE, 2021.
  46. Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, 102(5):699–716, 2014.
  47. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Frontiers in neuroscience, 9:141, 2015.
  48. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE transactions on biomedical circuits and systems, 12(1):106–122, 2017.
  49. Braindrop: A mixed-signal neuromorphic architecture with a dynamical systems-based programming model. Proceedings of the IEEE, 107(1):144–164, 2018.
  50. An accelerated analog neuromorphic hardware system emulating NMDA-and calcium-based non-linear dendrites. In 2017 International Joint Conference on Neural Networks (IJCNN), pages 2217–2226. IEEE, 2017.
  51. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front. Neurosci., 16(795876), feb 2022. 10.3389/fnins.2022.795876.
  52. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nature Electronics, pages 1–14, 2023.
  53. Brain-inspired hardware for artificial intelligence: Accelerated learning in a physical-model spiking neural network. Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, pages 119–122, 2019b. 10.1007/978-3-030-30487-4_10.
  54. Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1–5. IEEE, 2020.
  55. Structural plasticity on an accelerated analog neuromorphic hardware system. Neural Networks, 133:11 – 20, 2021. 10.1016/j.neunet.2020.09.024. 0893-6080.
  56. Accelerated physical emulation of bayesian inference in spiking neural networks. Frontiers in Neuroscience, 13:1201, 2019. 10.3389/fnins.2019.01201.
  57. Closed-loop experiments on the BrainScaleS-2 architecture. In Proceedings of the 2020 Annual Neuro-Inspired Computational Elements Workshop, pages 1–3, 2020.
  58. Korbinian Schreiber. Accelerated neuromorphic cybernetics. PhD thesis, Universität Heidelberg, January 2021.
  59. Fast and energy-efficient neuromorphic deep learning with first-spike times. Nature Machine Intelligence, 3:823–835, 2021. 10.1038/s42256-021-00388-x.
  60. Surrogate gradients for analog neuromorphic computing. Proceedings of the National Academy of Sciences, 119(4), 2022. 10.1073/pnas.2109194119.
  61. A neuromorphic vlsi head direction cell system. IEEE Transactions on Circuits and Systems I: Regular Papers, 58(1):150–163, 2010.
  62. A neuromorphic approach to path integration: a head-direction spiking neural network with vision-driven reset. In 2018 IEEE international symposium on circuits and systems (ISCAS), pages 1–5. IEEE, 2018b.
  63. An accelerated lif neuronal network array for a large-scale mixed-signal neuromorphic architecture. IEEE Transactions on Circuits and Systems I: Regular Papers, 65(12):4299–4312, 2018.
  64. Spinnaker 2: A 10 million core processor system for brain simulation and machine learning. arXiv preprint arXiv:1911.02385, 2019.
  65. Demonstrating hybrid learning in a flexible neuromorphic hardware system. IEEE transactions on biomedical circuits and systems, 11(1):128–142, 2016.
  66. Maplike representation of celestial e-vector orientations in the brain of an insect. Science, 315(5814):995–997, 2007.
  67. Sun compass integration of skylight cues in migratory monarch butterflies. Neuron, 69(2):345–358, 2011.
  68. Principles of neural science, volume 5. McGraw-hill New York, 2013.
  69. Eric Richard Kandel. Cellular basis of behavior: An introduction to behavioral neurobiology. W. H. Freeman, 1976.
  70. Quantitative neuroanatomy for connectomics in drosophila. Elife, 5:e12059, 2016.
  71. Presynaptic facilitation revisited: state and time dependence. Journal of Neuroscience, 16(2):425–435, 1996.
  72. Molecular Cell Biology. W. H. Freeman, 4 edition, 2000.
  73. A. M. Wenner. The flight speed of honeybees: a quantitative approach. Journal of Apicultural Research, 2(1):25–32, 1963.
  74. Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In Proceedings of IEEE international conference on evolutionary computation, pages 312–317. IEEE, 1996.
  75. Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system. In 2017 international joint conference on neural networks (IJCNN), pages 2227–2234. IEEE, 2017.
  76. Dale’s principle. Brain research bulletin, 50(5-6):349–350, 1999.
  77. When is an integrate-and-fire neuron like a poisson neuron? In Advances in neural information processing systems, pages 103–109, 1996.
  78. Firing frequency of leaky intergrate-and-fire neurons with synaptic current dynamics. Journal of theoretical Biology, 195(1):87–95, 1998.
  79. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT press, 2001.
  80. Dynamics of the firing probability of noisy integrate-and-fire neurons. Neural computation, 14(9):2057–2110, 2002.
  81. Role of synaptic filtering on the firing response of simple model neurons. Physical review letters, 92(2):028102, 2004.
  82. Stochastic inference with spiking neurons in the high-conductance state. Physical Review E, 94(4):042312, 2016.
  83. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of neurophysiology, 94(5):3637–3642, 2005.
  84. Firing patterns in the adaptive exponential integrate-and-fire model. Biological cybernetics, 99:335–347, 2008.
  85. Neural networks with dynamic synapses. Neural computation, 10(4):821–835, 1998.

Summary

No one has generated a summary of this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Summarize

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up