The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks

Horacio Rostro-Gonzalez, Bruno Cessac, Bernard Girau, Cesar Torres-Huitzil

Research output: Indexed journal article Articlepeer-review

10 Citations (Scopus)

Abstract

This paper presents a numerical analysis of the role of asymptotic dynamics in the design of hardware-based implementations of the generalised integrate-and-fire (gIF) neuron models. These proposed implementations are based on extensions of the discrete-time spiking neuron model, which was introduced by Soula et al., and have been implemented on Field Programmable Gate Array (FPGA) devices using fixed-point arithmetic. Mathematical studies conducted by Cessac have evidenced the existence of three main regimes (neural death, periodic and chaotic regimes) in the activity of such neuron models. These activity regimes are characterised in hardware by considering a precision analysis in the design of an architecture for an FPGA-based implementation. The proposed approach, although based on gIF neuron models and FPGA hardware, can be extended to more complex neuron models as well as to different in silico implementations.

Original languageEnglish
Pages (from-to)91-97
Number of pages7
JournalJournal of Physiology Paris
Volume105
Issue number1-3
DOIs
Publication statusPublished - Jan 2011
Externally publishedYes

Keywords

  • Computing with spikes
  • FPGA
  • GIF neuron models
  • Hardware implementations
  • Neural dynamics

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