Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks

Abstract

Emerging memory devices such as resistive switching memory (RRAM) and phase change memory (PCM) are gaining interest as future synapses for smart neuromorphic systems, capable of learning and inference similar to the human brain. Developing neuromorphic systems with emerging memory technologies requires accurate co-design of devices, synapses, and neural networks, aiming at the replication of the fundamental learning processes in the human brain, such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP). This work addresses the development of RRAM synapses for unsupervised learning via STDP. This learning scheme is implemented in a simple one-transistor/one-resistor (1T1R) structure capable of long term potentiation and depression with standard memory-grade RRAM devices. 1T1R synapses are implemented in a spiking neural network (SNN) with feedforward architecture, allowing for the hardware demonstration of unsupervised learning. Recurrent SNNs employing the same fundamental STDP rule are then addressed by simulation of associative learning, pattern reconstruction, and recall of spatiotemporal sequences.