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Evolving Memristive Neural Networks

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Handbook of Memristor Networks

Abstract

Of the many network representations in which memristors can be modelled, neural networks are perhaps the most enticing as they open the possibility for neuromorphic computing—biologically-inspired brainlike information processing in hardware. Memristors are analogous to biological synapses; both feature nonvolatile resistance, a charge-dependent plastic response to activity, and can provide adaptive learning when coupled with a Hebbian mechanism. In this chapter, various types of memristors are deployed as synapses in spiking networks. Biological information processing implies autonomous learning control—a neuro-evolutionary approach provides this functionality and is used to search for beneficial network topologies. The main focus of this work extends the remit of the evolutionary algorithm to alter the conductance profiles of individual memristors, creating networks of heterogeneous variable synapses. These variable memristor networks are tested against networks of benchmark synapses in a robotic pathfinding scenario. Experimental findings conclude that the variable synapses bestow more behavioural degrees of freedom to the networks, allowing them to outperform the comparative synapse types.

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Acknowledgements

This work was funded by EPSRC grant number EP/H014381/1.

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Authors and Affiliations

  1. University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol, BS161QY, UK

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

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  1. Gerard David Howard
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  2. Larry Bull
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  3. Ben De Lacy Costello
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  4. Ella Gale
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  5. Andrew Adamatzky
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Correspondence to Gerard David Howard .

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Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

    Leon Chua

  2. Democritus University of Thrace, Xanthi, Greece

    Georgios Ch. Sirakoulis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, UK

    Andrew Adamatzky

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Howard, G.D., Bull, L., De Lacy Costello, B., Gale, E., Adamatzky, A. (2019). Evolving Memristive Neural Networks. In: Chua, L., Sirakoulis, G., Adamatzky, A. (eds) Handbook of Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-76375-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-76375-0_24

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  • Online ISBN: 978-3-319-76375-0

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