Evolving spiking networks with variable resistive memories

Gerard Howard, Larry Bull, Ben de Lacy Costello, Ella Gale, Andrew Adamatzky

Research output: Contribution to journalLetter (Academic Journal)peer-review

22 Citations (Scopus)


Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

Original languageEnglish
Pages (from-to)79-103
Number of pages25
JournalEvolutionary Computation
Issue number1
Publication statusPublished - 2014


  • Genetic algorithms
  • Hebbian learning
  • Memristors
  • Neural networks
  • Nonvolatile memory
  • Self-adaptation


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