Modeling-based design of brain-inspired spiking neural networks with RRAM learning synapses

Brain-inspired computing is currently gaining momentum as a viable technology for artificial intelligence enabling recognition, language processing and online unsupervised learning. Brain-inspired circuit design is currently hindered by 2 fundamental limits: (i) understanding the event-driven spike processing in the human brain, and (ii) developing predictive models to design and optimize cognitive circuits. Here we present a comprehensive model for spiking neural networks based on spike-timing dependent plasticity (STDP) in resistive switching memory (RRAM) synapses. Both a Monte Carlo (MC) model and an analytical model are presented to describe experimental data from a state-of-the-art neuromorphic hardware. The model can predict the learning efficiency and time as a function of the input noise and pattern size, thus paving the way for model-based design of cognitive brain-like circuits.