Biorealistic spiking neural network on FPGA

In this paper, we present a digital hardware implementation of a biorealistic spiking neural network composed of 117 Izhikevich neurons. This digital system works in hard real-time, which means that it keeps the same biological time of simulation at the millisecond scale. The Izhikevich neuron implementation requires few resources. The neurons behavior is validated by comparing their firing rate to biological data. The interneuron connections are composed of biorealistic synapses. The architecture of the network implementation allows working on a single computation core. It is freely configurable from an independent-neuron configuration to all-to-all configuration or a mix with several independent small networks. This spiking neural network will be used for the development of a new proof-of-concept Brain Machine Interface, i.e. a neuromorphic chip for neuroprosthesis, which has to replace the functionality of a damaged part of the central nervous system.

[1]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[2]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[3]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[4]  A. Cassidy,et al.  FPGA Based Silicon Spiking Neural Array , 2007, 2007 IEEE Biomedical Circuits and Systems Conference.

[5]  Miguel A. L. Nicolelis,et al.  Principles of neural ensemble physiology underlying the operation of brain–machine interfaces , 2009, Nature Reviews Neuroscience.

[6]  Giacomo Indiveri,et al.  Synaptic Plasticity and Spike-based Computation in VLSI Networks of Integrate-and-Fire Neurons , 2007 .

[7]  Andrew S. Cassidy,et al.  Design of a one million neuron single FPGA neuromorphic system for real-time multimodal scene analysis , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[8]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[9]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[10]  A. Cassidy,et al.  Dynamical digital silicon neurons , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[11]  Henry Markram,et al.  Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons , 2008, Biological Cybernetics.

[12]  Terrence J. Sejnowski,et al.  An Efficient Method for Computing Synaptic Conductances Based on a Kinetic Model of Receptor Binding , 1994, Neural Computation.