Evolving Memristive Neural Networks

  • Gerard David HowardEmail author
  • Larry Bull
  • Ben De Lacy Costello
  • Ella Gale
  • Andrew Adamatzky


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.



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


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gerard David Howard
    • 1
    Email author
  • Larry Bull
    • 1
  • Ben De Lacy Costello
    • 1
  • Ella Gale
    • 1
  • Andrew Adamatzky
    • 1
  1. 1.University of the West of EnglandBristolUK

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