  Memristors have attracted interest as neuromorphic computation elements
because they show promise in enabling efficient hardware implementations of
artificial neurons and synapses. We performed measurements on interface-type
memristors to validate their use in neuromorphic hardware. Specifically, we
utilised Nb-doped SrTiO$_3$ memristors as synapses in a simulated neural
network by arranging them into differential synaptic pairs, with the weight of
the connection given by the difference in normalised conductance values between
the two paired memristors. This network learned to represent functions through
a training process based on a novel supervised learning algorithm, during which
discrete voltage pulses were applied to one of the two memristors in each pair.
To simulate the fact that both the initial state of the physical memristive
devices and the impact of each voltage pulse are unknown we injected noise at
each timestep. Nevertheless, discrete updates based on local knowledge were
shown to result in robust learning performance. Using this class of memristive
devices as the synaptic weight element in a spiking neural network yields, to
our knowledge, one of the first models of this kind, capable of learning to be
a universal function approximator, and strongly suggests the suitability of
these memristors for usage in future computing platforms.
