Work

Modeling artificial neuron spiking based on diffusive memristor with two filaments

Year: 2025

Type: article

Abstract: Diffusive memristors show great promise as fundamental components for brain-inspired neuromorphic computing. By relying on the drift and diffusion of charge carriers, which form conductive filaments for charge transport, these devices offer high nonlinearity, tunability, fast switching between resistive states, and low power consumption. Their ability to generate a wide range of nonlinear dynamics, driven by the complex interplay of thermal, electrical, and mechanical effects, mimics the behavior of biological neurons. In this paper, we simulate spiking dynamics in an artificial neuron based on diffusive memristors with two independent conducting filaments. We uncover instabilities that lead to self-sustained spike generation and show that external voltage bias allows the coexistence of two characteristic spiking modes. Noise, either inherent or externally added, facilitates switching between these spiking regimes. The model predictions align well with our experimental measurements, offering the way for the development of neuromorphic devices for parallel signal processing.

Source: APL Machine Learning

DOI: 10.1063/5.0294184

Authors: Amir Akther, Debi Pattnaik, Pavel Borisov, Sergey Savel’ev, Alexander G. Balanov

Institution: Loughborough University

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: Engineering and Physical Sciences Research Council

Award: Neuromorphic memristive circuits to simulate inhibitory and excitatory dynamics of neuron networks: from physiological similarities to deep learning