Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Neuromorphic systems increasingly attract research interest owing to their ability to provide biologically inspired methods of computing, alternative to the classic von Neumann architecture. In these systems, computing relies on spike-based communication between neurons, and memory is represented by evolving states of the synaptic interconnections. In this work, we first demonstrate how spike-timing-dependent plasticity (STDP) based synapses can be realized using the crystal-growth dynamics of phase-change memristors. Then, we present a novel learning architecture comprising an integrate-and-fire neuron and an array of phase-change synapses that is capable of detecting temporal correlations in parallel input streams. We demonstrate a continuous re-learning operation on a sequence of binary 20×20 pixel images in the presence of significant background noise. Experimental results using an array of phase-change cells as synaptic elements confirm the functionality and performance of the proposed learning architecture.