Dynamic learning of synchronization in coupled nonlinear systems

TL;DR

This paper introduces a novel machine learning-inspired optimization method called Dynamic Learning of Synchronization (DLS) that dynamically adjusts network weights to achieve and maintain synchronization in various coupled nonlinear systems.

Contribution

The paper develops and validates a new DLS technique that enhances synchronization in heterogeneous networks, addressing limitations of existing methods with adaptive, supervised, and hybrid variants.

Findings

DLS effectively synchronizes diverse network types.

DLS improves stability and adaptability of synchronization.

Validation on neural models demonstrates practical effectiveness.

Abstract

Synchronization phenomena are pervasive in coupled nonlinear systems across the natural world and engineering domains. Understanding how to dynamically identify the parameter space (or network structure) of coupled nonlinear systems in a synchronized state is crucial for the study of system synchronization. To address the challenge of achieving stable synchronization in coupled nonlinear systems, we develop a set of mathematical optimization techniques for dynamic learning of synchronization (DLS) inspired by machine learning. This technology captures the state differences between nodes within the system and dynamically adjusts weights, allowing coupled nonlinear systems to maintain a stable state of synchronization after appropriate weight adjustments. To enhance synchronization optimization, we use the Master Stability Function (MSF) to demonstrate how DLS effectively adjusts networks into their synchronization regions. We introduce several variants of the DLS technique, including adaptive, supervised, and hybrid methods, effectively promoting synchronization in heterogeneous networks such as small-world, scale-free, and random networks. The efficacy of this technique is validated through its application to simple FitzHugh-Nagumo neural networks and complex Hodgkin-Huxley neuronal networks, examining its impact on both global and local synchronization. The DLS technique proposed in this study offers a new solution to synchronization problems in dynamic network environments, addressing the deficiencies in adaptability and flexibility of existing technologies and providing a fresh perspective for understanding and implementing synchronization phenomena in coupled nonlinear systems.