  Neuromorphic computing --- brainlike computing in hardware --- typically
requires myriad CMOS spiking neurons interconnected by a dense mesh of
nanoscale plastic synapses. Memristors are frequently citepd as strong synapse
candidates due to their statefulness and potential for low-power
implementations. To date, plentiful research has focused on the bipolar
memristor synapse, which is capable of incremental weight alterations and can
provide adaptive self-organisation under a Hebbian learning scheme. In this
paper we consider the Unipolar memristor synapse --- a device capable of
non-Hebbian switching between only two states (conductive and resistive)
through application of a suitable input voltage --- and discuss its suitability
for neuromorphic systems. A self-adaptive evolutionary process is used to
autonomously find highly fit network configurations. Experimentation on a two
robotics tasks shows that unipolar memristor networks evolve task-solving
controllers faster than both bipolar memristor networks and networks containing
constant nonplastic connections whilst performing at least comparably.
