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Evolving unipolar memristor spiking neural networks

Howard, Gerard David; Bull, Larry; De Lacy Costello, Ben


Gerard David Howard

Lawrence Bull
AHOD Research and Scholarship and Prof


© 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.


Howard, G. D., Bull, L., & De Lacy Costello, B. (2015). Evolving unipolar memristor spiking neural networks. Connection Science, 27(4), 397-416.

Journal Article Type Article
Acceptance Date Jul 2, 2015
Publication Date Oct 2, 2015
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
Keywords evolution
Public URL
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