Low Power Stochastic Neurons From SiO2-Based Bilayer Conductive Bridge Memristors for Probabilistic Spiking Neural Network Applications—Part II: Modeling

Abstract

A deep understanding of the underlying resistive switching mechanism for the implementation of volatile memristive properties is regarded as of great importance for enhancing their performance. Along these lines, a 2-D dynamical model is introduced to interpret the whole memristive pattern within the bilayer configuration, as well as the crucial of the dense layer of the Pt nanoparticles (NPs) on the local thermal distribution. Moreover, the probabilistic leaky-integrate-and-fire (LIF) neuron properties were simulated by considering a simple <inline-formula> <tex-math notation="LaTeX">$\textit {RC}$ </tex-math></inline-formula> circuit in order to perform Bayesian extrapolation within a spiking neural network. A classification application is consequently demonstrated by using the liver tumor dataset. The advantageous capabilities of the stochastic-based spike neural networks (SNNs) are highlighted in striking contrast with the conventional artificial neural networks (ANNs), as well as the deterministic-based SNNs, in terms of prediction accuracy and power consumption.