Optimal Tuning of Memristor Conductance Variation in Spiking Neural Networks for Online Unsupervised Learning

In unsupervised learning (UL)-oriented spiking neural network (SNN) designs, the initial conductance variation of the synapses is usually necessary, with which temporally differentiated responses of the output neurons can be achieved given the same input patterns, in order to implement the important lateral inhibition function. We take a lithium silicate memristor as an example and demonstrate through a motion-style recognition task that, without regulating the initial conductance variation of memristor synapses, several types of errors will occur: too large conductance variance leads to the deactivation of some output neurons and the incomplete learning of the motion routes, while too small variance causes duplicated learnings of the same pattern. By tuning the memristor conductance variance through the forming voltages, it is found that the UL capacity can be optimized, indicating a promising approach to enhance the UL performance of a memristor-based SNN.