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Conference Papers Year : 2013

Biorealistic Spiking Neural Network on FPGA

Abstract

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.

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Bioengineering
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Dates and versions

hal-00956630 , version 1 (07-03-2014)

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Matthieu Ambroise, Timothée Levi, Yannick Bornat, Sylvain Saighi. Biorealistic Spiking Neural Network on FPGA. Information Sciences and Systems (CISS), 2013 47th Annual Conference on, Mar 2014, Baltimore, United States. pp.1-6, ⟨10.1109/CISS.2013.6616689⟩. ⟨hal-00956630⟩
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