Detecting Correlations Using Phase-Change Neurons and Synapses

Abstract

As the conventional von Neumann-based computational architectures reach their scalability and performance limits, alternative computational frameworks inspired by biological neuronal networks hold promise to revolutionize the way we process information. Here, we present a bioinspired computational primitive that utilizes an artificial spiking neuron equipped with plastic synapses to detect temporal correlations in data streams in an unsupervised manner. We demonstrate that the internal states of the neuron and of the synapses can be efficiently stored in nanoscale phase-change memory devices and show computations with collocated storage in an experimental setting.