Novel Causal Relations between Neuronal Networks due to Synchronization

Abstract

Abstract In the process of information transmission, information is thought to be transmitted from the networks that are activated by the input to the networks that are silent or nonactivated. Here, via numerical simulation of a 3-network motif, we show that the silent neuronal network when interconnected with other 2 networks can exert much stronger causal influences on the other networks. Such an unexpected causal relationship results from high degree of synchronization in this network. The predominant party is consistently the network whose noise is smaller when the noise level in each network is considered. Our results can shed lights on how the internal network dynamics can affect the information flow of interconnected neuronal networks.