Noise-Driven Synchronization of Coupled Neural Networks

Abstract

The brain is complex network of dynamical system. For this reason, the interaction between different brain areas can be modeled as a large-scale network which is important in functional brain dynamics, and it closely related to the brain disorder. In recent years, the significant progress about the understanding of the relation between the structure and dynamical properties of the networks has developed. It describes that the complex dynamic behavior such as synchronization of coupled dynamical system plays a crucial role in a brain function and dysfunction. In our simulations, we construct a coupled neural network that are in different activity states, the synchronous firing state (SFS) network represents the healthy nerve cells and background activity state (BAS) network represents the damaged nerve cells. We identify their interaction by varying the synaptic coupling strength of inter-network interactions. The Hodgkin-Huxley (HH) neuronal model is biologically plausible and spike timing dependent plasticity (STDP) or inverse STDP mechanisms are considered restructuring the coupling strength of the synapses. In this study, we find that SFS network can induce the synchronous firing in the BAS network by enhancing the coupling strength between the coupled networks. Here, we identify the threshold of coupling strength, in which the BAS network keep their synchronous state after disconnecting the inter-connections from SFS network. Thus, this method can be considered as a new treatment to repair a damage brain after injury or because of some disease. Then, we analyze the network properties such as the degree distribution, the characteristic path length and the cluster coefficient of the network to propose the design of intra-network neurons distribution and the design of the inter-connection probability which can give a better result than random neurons distribution or random inter-network connections.