Intrinsic Neuro-Synaptic Spiking Dynamics and Resonance in Memristive Networks

2026-04-20

AI Summary

This study investigates how memristive networks spontaneously generate biologically plausible neuronal population spiking dynamics and elucidates the underlying mechanisms. Through nonlinear dynamical analysis, circuit simulations, and heterogeneous network topology modeling, the research systematically examines the self-organized synaptic behavior of such networks under both DC and AC inputs. The work reveals, for the first time, an intrinsic nonlinear resonance phenomenon in memristive networks and demonstrates that spike synchronization is significantly enhanced when the driving signal frequency matches the network’s intrinsic timescale. Furthermore, it identifies the computationally optimal frequency at the point of maximal response just prior to the onset of resonance. By successfully reproducing neurobiologically realistic spiking dynamics, this study establishes a novel dynamical foundation for brain-inspired computing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Social media analysis through the lenses of networks

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.

Problem

• memristive networks
• neuro-synaptic dynamics
• spiking dynamics
• nonlinear resonance
• intrinsic timescale

Innovation

• memristive networks
• neuro-synaptic dynamics
• spiking dynamics
• nonlinear resonance
• emergent computation