Intrinsic Neuro-Synaptic Spiking Dynamics and Resonance in Memristive Networks

TL;DR

This paper explores how memristive networks naturally produce neuron-like spiking and resonance behaviors, with optimal computational frequencies linked to their intrinsic dynamics.

Contribution

It demonstrates the intrinsic neuro-synaptic dynamics in memristive networks and their resonance phenomena, combining mathematical and numerical analysis.

Findings

Networks generate neuron-like spiking dynamics.

Nonlinear resonance occurs at the network's intrinsic frequency.

Optimal computational frequency is just below resonance onset.

Abstract

Self-organizing memristive networks are physical circuits that dynamically reconfigure their circuitry in response to external input signals. Their adaptive behavior arises from intrinsic neuro-synaptic dynamics combined with a heterogeneous network topology. In this work, we demonstrate that such networks naturally generate neuronal population spiking dynamics similar to those observed in biological neuronal systems. This study investigates the intrinsic and emergent dynamics of memristive networks mathematically and numerically for both DC and AC input signals. Nonlinear spike-like features are maximized when the frequency of the input driving signal matches the network's intrinsic dynamical timescale, where nonlinear resonance is observed. Furthermore, the optimal frequency for computation is found to be the maximal frequency before the onset of resonance.