Exploiting memristance for implementing spike-time-dependent-plasticity in neuromorphic nanotechnology systems

Abstract

In this paper we show that STDP can be implemented using a crossbar memristive array combined with neurons that asynchronously generate spikes of a given shape. An attenuated version of such spikes needs to be sent back through the neurons input terminal. The shape of the spikes turns out to be very similar to the neural spikes observed in biology for real neurons. The STDP learning function obtained by combining such neurons with memristors is exactly that of the STDP learning function obtained from neurophysiological experiments on real synapses.