Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity

Abstract

Neural networks with resistive-switching memory (RRAM) synapses can mimic learning and recognition in the human brain, thus overcoming the major limitations of von Neumann computing architectures. While most researchers aim at supervised learning of a pre-determined set of patterns, unsupervised learning of patterns might be attractive for brain-inspired robot/drone navigation. Here we demonstrate neural networks with CMOS/RRAM synapses capable of unsupervised learning by spike-time dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP). First, STDP learning in a RRAM synaptic network is demonstrated. Then we present a 4-transistor/1-resistor synapse capable of SRDP, finally demonstrating SRDP learning, update, and recognition of patterns at the level of neural network.