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

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 RC 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.

Automated summary

A 2-D dynamical model is proposed to explain the memristive properties in a bilayer structure and investigate the influence of a dense layer of Pt nanoparticles on thermal distribution. By simulating probabilistic leaky-integrate-and-fire neuron properties, Bayesian extrapolation is achieved in a spiking neural network. The classification application using a liver tumor dataset highlights the advantages of stochastic-based spike neural networks in terms of accuracy and power consumption compared to conventional artificial neural networks and deterministic-based spike neural networks.