Modular memristor model with synaptic-like plasticity and volatile memory

TL;DR

This paper presents a modular, physics-inspired memristor model that captures complex dynamics like volatility and synaptic plasticity, validated against experimental data for neuromorphic system simulation.

Contribution

The authors develop a comprehensive, efficient memristor model integrating volatility, saturation, and synaptic-like plasticity within a unified framework, advancing neuromorphic hardware design.

Findings

Model accurately fits experimental data from polymeric memristors.

Incorporates synaptic-like plasticity based on STDP rules.

Provides a scalable approach for large neuromorphic system simulations.

Abstract

Compact models of memristors are essential for simulating large-scale neuromorphic systems, yet they often do not include description of complex dynamics like volatile relaxation and synaptic plasticity. We introduce a modular, computationally efficient memristor model that bridges this gap by integrating principles from physics and computational neuroscience. The model defines a framework consisting of a standard formulation of memristive device dynamics, a functional rule mapping state variables to cumulative conductance, a volatility module inspired by the theory of linear viscoelasticity and a saturation module implementing a linear-nonlinear technique. Additionally, we develop a formulation of synaptic-like plasticity inspired by a biological spike-timing-dependent plasticity (STDP) rule, which is compatible with the general framework for memristive devices. Finally, we propose a Laplace transform-based technique to derive the precise form of the mapping from state variables to cumulative conductance, replacing ad hoc voltage-current relationships with principled construction. We quantitatively validate the complete model against a rich set of experimental data from polymeric memristors exhibiting potentiation, synaptic-like plasticity and volatile decay. Our work presents a new paradigm for memristor modeling that is both practical for large-scale simulation and rich in explanatory power, providing a principled tool for the design of next-generation neuromorphic hardware.