Stochastic Instabilities of the Diffusive Memristor

TL;DR

This paper investigates the stochastic and bifurcation behaviors of diffusive memristors, combining theoretical modeling with experimental validation to understand their complex spike dynamics relevant for neuromorphic applications.

Contribution

It introduces a stochastic differential equation model for diffusive memristors and analyzes noise-induced bifurcations, supported by experimental spiking data.

Findings

Identification of classical and noise-induced bifurcations in memristor dynamics

Demonstration of stochastic threshold phenomena through experiments

Insights into the complex spike behaviors relevant for neuromorphic systems

Abstract

Recently created diffusive memristors have garnered significant research interest owing to their distinctive capability to generate a diverse array of spike dynamics which are similar in nature to those found in biological cells. This gives the memristor an opportunity to be used in a wide range of applications, specifically within neuromorphic systems. The diffusive memristor is known to produce regular, chaotic and stochastic behaviors which leads to interesting phenomena resulting from the interactions between the behavioral properties. The interactions along with the instabilities that lead to the unique spiking phenomena are not fully understood due to the complexities associated with examining the stochastic properties within the diffusive memristor. In this work, we analyze both the classical and the noise induced bifurcations that a set of stochastic differential equations, justified through a Fokker-Planck equation used to model the diffusive memristor, can produce. Finally, we replicate the results of the numerical stochastic threshold phenomena with experimentally measured spiking.