An FPGA-based hardware-efficient fault-tolerant astrocyte-neuron network

Anju P. Johnson, David M. Halliday, Alan G. Millard, Andy M. Tyrrell, Jon Timmis, Junxiu Liu, Jim Harkin, Liam McDaid, Shvan Karim

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

18 Citations (SciVal)

Abstract

The human brain is structured with the capacity to repair itself. This plasticity of the brain has motivated researchers to develop systems which have similar capabilities of fault tolerance and self-repair. Recent research findings have proven that interactions between astrocytes and neurons can actuate brain-like self-repair in a bidirectionally coupled astrocyte-neuron system. This paper presents a hardware realization of the bio-inspired self-repair architecture on an FPGA. We also introduce a reduced architecture for an FPGA-based hardware-efficient fault-tolerant system. This is based on the principle of retrograde signaling in an astrocyte-neuron network by simplifying the calcium dynamics within the astrocyte. The hardware optimized implementation shows more than a 90% decrease in hardware utilization and proves an efficient implementation for a large-scale astrocyte-neuron network. An Average spike rate of 0:027 spikes per clock cycle were observed for both the proposed models of astrocytes in the case of 100% partial fault.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherIEEE Press
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 09 Feb 2017
Externally publishedYes
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 06 Dec 201609 Dec 2016

Publication series

Name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/TerritoryGreece
CityAthens
Period06 Dec 201609 Dec 2016

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