An FPGA-based Hardware-Efficient Fault-Tolerant Astrocyte-Neuron Network

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

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

8 Citations (Scopus)

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)
PublisherIEEE
ISBN (Electronic)9781509042401
ISBN (Print)9781509042418
DOIs
Publication statusPublished - 13 Feb 2017
Externally publishedYes
EventIEEE Symposium Series on Computational Intelligence - Athens, Greece
Duration: 6 Dec 20169 Dec 2016
https://ewh.ieee.org/conf/ssci/2016/ssci-2016-cfp.pdf (Link to Call for Papers)

Conference

ConferenceIEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI
CountryGreece
CityAthens
Period6/12/169/12/16
Internet address

Fingerprint

Neurons
Field programmable gate arrays (FPGA)
Hardware
Repair
Brain
Fault tolerance
Plasticity
Astrocytes
Clocks
Calcium

Cite this

Johnson, A., Halliday, D., Millard, A., Tyrrell, A., Timmis, J., Liu, J., ... Karim, S. (2017). An FPGA-based Hardware-Efficient Fault-Tolerant Astrocyte-Neuron Network. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) IEEE. https://doi.org/10.1109/SSCI.2016.7850175
Johnson, Anju ; Halliday, David ; Millard, Alan ; Tyrrell, Andy ; Timmis, Jon ; Liu, Junxiu ; Harkin, Jim ; McDaid, Liam ; Karim, Shvan. / An FPGA-based Hardware-Efficient Fault-Tolerant Astrocyte-Neuron Network. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017.
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Johnson, A, Halliday, D, Millard, A, Tyrrell, A, Timmis, J, Liu, J, Harkin, J, McDaid, L & Karim, S 2017, An FPGA-based Hardware-Efficient Fault-Tolerant Astrocyte-Neuron Network. in 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, IEEE Symposium Series on Computational Intelligence, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7850175

An FPGA-based Hardware-Efficient Fault-Tolerant Astrocyte-Neuron Network. / Johnson, Anju; Halliday, David; Millard, Alan; Tyrrell, Andy ; Timmis, Jon; Liu, Junxiu; Harkin, Jim; McDaid, Liam; Karim, Shvan.

2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017.

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

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Johnson A, Halliday D, Millard A, Tyrrell A, Timmis J, Liu J et al. An FPGA-based Hardware-Efficient Fault-Tolerant Astrocyte-Neuron Network. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 2017 https://doi.org/10.1109/SSCI.2016.7850175