Abstract
Models of neural systems often use idealized inputs and outputs, but there is also much to learn by forcing a neural model to interact with a complex simulated or physical environment. Unfortunately, sophisticated interactions require models of large neural systems, which are difficult to run in real time. We have prototyped a system that can simulate efficient surrogate models of a wide range of neural circuits in real time, with a field programmable gate array (FPGA). The scale of the simulations is increased by avoiding simulation of individual neurons, and instead simulating approximations of the collective activity of groups of neurons. The system can approximate roughly a million spiking neurons in a wide range of configurations.
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References
Bekolay, T., et al.: Nengo: a Python tool for building large-scale functional brain models. Front. Neuroinformatics 7(48), 1–13 (2014)
Cassidy, A., et al.: Design of a one million neuron single fpga neuromorphic system for real-time multimodal scene analysis. In: 2011 45th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2011)
Choudhary, S., et al.: Silicon neurons that compute. In: International Conference on Artificial Neural Networks, pp. 121–128 (2012)
Eliasmith, C.: A unified approach to building and controlling spiking attractor networks. Neural Comput. 17(6), 1276–1314 (2005)
Eliasmith, C., Anderson, C.H.: Developing and appl a toolkit from a general neurocomputational framework. Neurocomputing 26, 1013–1018 (1999)
Eliasmith, C., Anderson, C.H.: Neural Engineering: Computation, Representation and Dynamics in Neurobiological Systems. MIT Press, Cambridge (2003)
Eliasmith, C., Stewart, T.C., Choo, X., Bekolay, T., DeWolf, T., Tang, C., Rasmussen, D.: A large-scale model of the functioning brain. Science 338(6111), 1202–1205 (2012)
Eliasmith, C., et al.: A large-scale model of the functioning brain. Science 338, 1202–1205 (2012)
Georgopoulos, A., et al.: Mental rotation of the neuronal population vector. Science 243, 234–236 (1989)
Jonas, P., et al.: Quantal components of unitary EPSCs at the mossy fibre synapse on CA3 pyramidal cells of rat hippo. J. Physio. 472(1), 615–663 (1993)
Li, J., et al.: An FPGA-based silicon neuronal network with selectable excitability silicon neurons. Front. Neuroscience 6(183), 1 (2012)
Merolla, P., et al.: Artificial brains: a million spiking-neuron IC with a scalable communication network and interface. Science 345(6197), 668–673 (2014)
Sah, P., et al.: Properties of excitatory postsynaptic currents recorded in vitro from rat hippocampal interneurones. J. Physio. 430(1), 605–616 (1990)
Tripp, B.P.: Surrogate population models for large-scale neural simulations. Neural Comput. 27(6), 1186–1222 (2015)
Wang, R., et al.: An FPGA implementation of a polychronous spiking neural network with delay adaptation. Front. Neuroscience 7(14), 1 (2013)
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Berzish, M., Eliasmith, C., Tripp, B. (2016). Real-Time FPGA Simulation of Surrogate Models of Large Spiking Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_41
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DOI: https://doi.org/10.1007/978-3-319-44778-0_41
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