Stochastic Dynamics of Diffusive Memristor Blocks for Neuromorphic Computing

TL;DR

This paper investigates the stochastic behavior of diffusive memristor-based neural circuits, combining modeling and experiments to understand their computational capabilities for neuromorphic computing.

Contribution

It introduces a simplified neural circuit model using diffusive memristors and demonstrates how specific input patterns control spiking, advancing hardware implementations of neural functions.

Findings

Numerical simulations align with experimental results.

Input voltage combinations can selectively activate neuron spiking.

Statistical analysis of spiking patterns reveals computational insights.

Abstract

Biological systems use neural circuits to integrate input information and produce outputs. Synaptic convergence, where multiple neurons converge their inputs onto a single downstream neuron, is common in natural neural circuits. However, understanding specific computations performed by such neural blocks and implementating them in hardware requires further research. This work focuses on synaptic convergence in a simplified circuit of three spiking artificial neurons based on diffusive memristors. Numerical modelling and experiments reveal input voltage combinations that enable targeted activation of spiking for specific neuron configurations. We analyse the statistical characteristics of spiking patterns and interpret them from a computational perspective. The numerical simulations match experimental measurements. Our findings contribute to development of universal functional blocks for neuromorphic systems.