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Evolution of plastic learning in spiking networks via memristive connections

Howard, Gerard; Howard, David; Gale, Ella; Bull, Larry; Adamatzky, Andrew; De Lacy Costello, Ben

Authors

Gerard Howard

David Howard

Ella Gale

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor



Abstract

This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and interneural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of: 1) linear resistors, and 2) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks. © 2012 IEEE.

Citation

Howard, G., Howard, D., Gale, E., Bull, L., De Lacy Costello, B., & Adamatzky, A. (2012). Evolution of plastic learning in spiking networks via memristive connections. IEEE Transactions on Evolutionary Computation, 16(5), 711-729. https://doi.org/10.1109/TEVC.2011.2170199

Journal Article Type Article
Publication Date Oct 9, 2012
Deposit Date Jan 21, 2013
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 16
Issue 5
Pages 711-729
DOI https://doi.org/10.1109/TEVC.2011.2170199
Keywords genetic algorithms, hebbian theory, memristors, neurocontrollers
Public URL https://uwe-repository.worktribe.com/output/949859
Publisher URL http://dx.doi.org/10.1109/TEVC.2011.2170199