Synchronization in multiplex neural networks with homeostatic structural plasticity

Abstract

The brain has a hierarchical structure with multiple connection types, which is best represented by a multiplex network structure, namely a network composed of two or more different layers. In order to further explore the synchronization phenomenon in biological neural networks, a bi-layer neural network with homeostatic structural plasticity coupled by electrical synapses is constructed in this paper. In addition, this study introduces the synchronization factor and basin stability to measure the network synchronization and quantify the stability of the synchronization state, respectively. The results show that increasing the intra-layer coupling strength, average degree and rewiring frequency can lead to a higher and more stable synchronization network. Moreover, increasing the inter-layer coupling strength and the number of inter-layer connections can improve the synchronization and stability in bi-layer networks. In particular, larger inter-layer coupling strength leads to the sub-network synchronization and stability much better than in the corresponding isolated network, and the higher the degree of synchronization of another sub-network can promote the synchronization and stability of the sub-network more. This study may provide useful guidance for the study of physiological functions related to brain synchronization.