  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.
