Characterizing Memristive Nanowire Network Models via a Unified Computational Framework

TL;DR

This paper introduces MemNNetSim, a versatile Python-based computational framework for simulating and analyzing memristive nanowire networks, facilitating research into their complex dynamics and potential in reservoir computing.

Contribution

The paper presents a novel unified simulation framework for memristive nanowire networks, enabling analysis of static and dynamic behaviors under various models.

Findings

Demonstrated utility of MemNNetSim in simulating NWNs

Enabled exploratory analysis for reservoir computing applications

Facilitated understanding of NWN dynamics

Abstract

Randomly self-assembled nanowire networks (NWNs) are dynamical systems in which junctions between two nanowires can be modelled as memristive units viewed as adaptive resistors with memory. Various memristive models have been proposed to capture the complex mechanics of these junctions. Here, we showcase a novel computational framework named Memristive Nanowire Network Simulator (MemNNetSim) to simulate and analyze random memristive NWNs in a unified approach. Implemented using the Python programming language, MemNNetSim allows for the analysis of static and dynamic scenarios of NWNs under arbitrary memristive models. This provides a versatile foundation to build upon in further work, such as reservoir dynamics with NWNs, which has seen increased interest due to the interconnected architecture of NWNs. In this work, we introduce the package, demonstrate its utility in simulating NWNs, and then test advanced scenarios in which it can aid in the exploratory analysis of these systems, particularly in learning how to use NWNs as a physical reservoir in reservoir computing applications.