Attractor networks and associative memories with STDP learning in RRAM synapses

Abstract

Attractor networks can realistically describe neurophysiological processes while providing useful computational modules for pattern recognition, signal restoration, and feature extraction. To implement attractor networks in small-area integrated circuits, the development of a hybrid technology including CMOS transistors and resistive switching memory (RRAM) is essential. This work presents a summary of recent results toward implementing RRAM-based attractor networks. Based on realistic models of HfO2 RRAM devices, we design and simulate recurrent networks showing the capability to train, recall and sustain attractors. The results support the feasibility of RRAM-based bio-realistic attractor networks.