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. 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.
@article{arxiv.1505.04357,
title = {Evolving Spiking Networks with Variable Resistive Memories},
author = {Gerard David Howard and Larry Bull and Ben de Lacy Costello and Andrew Adamatzky and Ella Gale},
journal= {arXiv preprint arXiv:1505.04357},
year = {2015}
}