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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

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

Johnson, A.P., Halliday, D.M., Millard, A.G., Tyrrell, A.M., Timmis, Jonathan, Liu, J., Harkin, J., McDaid, L. and Karim, S. (2017) An FPGA-based hardware-efficient fault-tolerant astrocyte-neuron network. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). UNSPECIFIED. ISBN 9781509042401

Item Type: Book Section


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.

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Depositing User: Jonathan Timmis


Item ID: 11572
Identification Number:
ISBN: 9781509042401
Official URL:

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Date Deposited: 27 Feb 2020 14:28
Last Modified: 27 Feb 2020 14:28


Author: A.P. Johnson
Author: D.M. Halliday
Author: A.G. Millard
Author: A.M. Tyrrell
Author: Jonathan Timmis
Author: J. Liu
Author: J. Harkin
Author: L. McDaid
Author: S. Karim

University Divisions

Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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