Controlling network-coupled neural dynamics with nonlinear network control theory

TL;DR

This paper develops a control strategy for complex nonlinear neural networks using Lyapunov methods, providing theoretical guarantees and validating effectiveness through numerical experiments on neural dynamics models.

Contribution

It introduces a novel control approach for nonlinear network-coupled neural systems with proven controllability and demonstrates its effectiveness via numerical simulations.

Findings

Control strategy guarantees controllability of neural dynamics.

Numerical experiments confirm the strategy's effectiveness.

Applicable to models of phase synchronization and neural populations.

Abstract

This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics. Based on the Lyapunov direct method, we derive a control strategy with theoretical guarantees of controllability. To verify the performance of the derived control strategy, we perform numerical experiments on two nonlinear network-coupled dynamical systems that emulate phase synchronization and neural population dynamics. The results demonstrate the feasibility and effectiveness of our control strategy.