Effects of degree distribution in mutual synchronization of neural networks

TL;DR

This paper investigates how the degree distribution affects mutual synchronization in two-layer neural networks, highlighting the importance of high-degree node couplings and strategies to control synchronization.

Contribution

It introduces analysis of different coupling strategies and demonstrates the critical role of large-degree node couplings in neural network synchronization.

Findings

Large-large coupling reduces the number of couplings needed for synchronization.

Cutting large-degree node couplings effectively prevents synchronization.

Scale-free networks are particularly sensitive to coupling strategies.

Abstract

We study the effects of the degree distribution in mutual synchronization of two-layer neural networks. We carry out three coupling strategies: large-large coupling, random coupling, and small-small coupling. By computer simulations and analytical methods, we find that couplings between nodes with large degree play an important role in the synchronization. For large-large coupling, less couplings are needed for inducing synchronization for both random and scale-free networks. For random coupling, cutting couplings between nodes with large degree is very efficient for preventing neural systems from synchronization, especially when subnetworks are scale-free.