Non-Bernoulli operations of stochastic binary synapses using resistive switching devices for unsupervised learning in a spiking neural network

Abstract

We study the performance of unsupervised learning using a stochastic synaptic update rule in a spiking neural network with binary synapses. For the binary synapse, we propose a circuit that employs a pair of resistive switching devices with switching properties described by a Weibull distribution. Through simulations, we find that the learning performance is much better with a shape parameter larger than 1, in which case the synaptic operations are regarded as non-Bernoulli stochastic trials, than with a simple Bernoulli model, and that the performance is even better than that achieved using a deterministic rule with continuous synaptic weights.