A Compact Model for Stochastic Spike-Timing-Dependent Plasticity (STDP) Based on Resistive Switching Memory (RRAM) Synapses

Abstract

Resistive switching memory (RRAM) devices have been proposed to boost the density and the bio-realistic plasticity in neural networks. One of the main limitations to the development of neuromorphic systems with RRAM devices is the lack of compact models for the simulation of spiking neural networks, including neuron spike processing, synaptic plasticity, and stochastic learning. Here, we present a predictive model for neuromorphic networks with unsupervised spike timing-dependent plasticity (STDP) in HfO2 RRAM devices. Our compact model can predict the learning behavior of experimental networks and can speed up the simulation of unsupervised learning compared to Monte Carlo (MC) approaches. The model can be used to optimize the classification accuracy of data sets, such as MNIST, and to estimate the time of learning and the energy consumption.