Gerard David Howard
Evolving unipolar memristor spiking neural networks
Howard, Gerard David; Bull, Larry; De Lacy Costello, Ben
Authors
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Benjamin De Lacy Costello Ben.DeLacyCostello@uwe.ac.uk
Associate Professor in Diagnostics and Bio-Sensing Technology
Abstract
© 2015 Taylor & Francis. Neuromorphic computing – brain-like computing in hardware – typically requires myriad complimentary metal oxide semiconductor spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently cited as strong synapse candidates due to their statefulness and potential for low-power implementations. To date, plentiful research has focused on the bipolar memristor synapse, which is capable of incremental weight alterations and can provide adaptive self-organisation under a Hebbian learning scheme. In this paper, we consider the unipolar memristor synapse – a device capable of non-Hebbian switching between only two states (conductive and resistive) through application of a suitable input voltage – and discuss its suitability for neuromorphic systems. A self-adaptive evolutionary process is used to autonomously find highly fit network configurations. Experimentation on two robotics tasks shows that unipolar memristor networks evolve task-solving controllers faster than both bipolar memristor networks and networks containing constant non-plastic connections whilst performing at least comparably.
Citation
Howard, G. D., Bull, L., & De Lacy Costello, B. (2015). Evolving unipolar memristor spiking neural networks. Connection Science, 27(4), 397-416. https://doi.org/10.1080/09540091.2015.1080225
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 2, 2015 |
Publication Date | Oct 2, 2015 |
Deposit Date | Sep 7, 2015 |
Publicly Available Date | Mar 28, 2024 |
Journal | Connection Science |
Print ISSN | 0954-0091 |
Electronic ISSN | 1360-0494 |
Publisher | Taylor & Francis |
Peer Reviewed | Peer Reviewed |
Volume | 27 |
Issue | 4 |
Pages | 397-416 |
DOI | https://doi.org/10.1080/09540091.2015.1080225 |
Keywords | evolution |
Public URL | https://uwe-repository.worktribe.com/output/805830 |
Publisher URL | http://dx.doi.org/10.1080/09540091.2015.1080225 |
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