FPGA based silicon spiking neural array

Andrew Cassidy, Susan Denham, Patrick Kanold, Andreas Andreou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations

Abstract

Rapid design time, low cost, flexibility, digital precision, and stability are characteristics that favor FPGAs as a promising alternative to analog VLSI based approaches for designing neuromorphic systems. High computational power as well as low size, weight, and power (SWAP) are advantages that FPGAs demonstrate over software based neuromorphic systems. We present an FPGA based array of Leaky-Integrate and Fire (LIF) artificial neurons. Using this array, we demonstrate three neural computational experiments: auditory Spatio-Temporal Receptive Fields (STRFs), a neural parameter optimizing algorithm, and an implementation of the Spike Time Dependant Plasticity (STDP) learning rule.

Original languageEnglish (US)
Title of host publicationConference Proceedings - IEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007
Pages75-78
Number of pages4
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
EventIEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007 - Montreal, QC, Canada
Duration: Nov 27 2007Nov 30 2007

Publication series

NameConference Proceedings - IEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007

Conference

ConferenceIEEE Biomedical Circuits and Systems Conference Healthcare Technology, BiOCAS2007
Country/TerritoryCanada
CityMontreal, QC
Period11/27/0711/30/07

ASJC Scopus subject areas

  • Hardware and Architecture
  • Biomedical Engineering

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