Synchronization of Intermittently Coupled Neural Networks With Coupling Delay

Area of Science:

• Complex Systems
• Computational Neuroscience
• Control Theory

Background:

• Synchronization of coupled neural networks (CNNs) is a key research area.
• Existing studies often assume continuous couplings, which is unrealistic.
• Intermittent couplings and coupling delays present significant challenges in network synchronization.

Purpose of the Study:

• To address the synchronization problem in intermittently coupled neural networks (ICNNs) with coupling delay.
• To develop novel theoretical frameworks for analyzing ICNN synchronization under realistic intermittent coupling conditions.
• To establish synchronization criteria that are independent of coupling delay and do not require external control.

Main Methods:

• Development of a general piecewise delay differential inequality to model dynamics during coupled and decoupled intervals.
• Derivation of delay-independent synchronization criteria (DISCs) for ICNNs.
• Formulation of non-linear matrix inequality (LMI)-based delay-dependent synchronization criteria (DDSCs) for specific cases.

Main Results:

• Established delay-independent synchronization criteria (DISCs) applicable to general coupling delays in ICNNs.
• Demonstrated that synchronization can be achieved without external control.
• Developed computationally efficient, delay-dependent synchronization criteria (DDSCs) using LMIs, without requiring delay differentiability.

Conclusions:

• The proposed methods effectively address the synchronization of ICNNs with coupling delays.
• The developed criteria offer robust and practical solutions for analyzing intermittent network synchronization.
• The findings advance the understanding of complex network dynamics and synchronization phenomena.