Dynamic Pathway Selection Mechanisms of Brain Networks

Abstract

Based on the dynamic reorganization mechanism of brain science and the fact that synaptic adaptability is affected by synaptic type, synaptic number and ion concentration, a bionic dynamic synaptic model is proposed and applied to a motif model and brain-like network model. By extracting the phase synchronization characteristics of the neural signals of node pairs in time sequence, and then deeply studying the regulation and control effect of synchronous discharge activities on effective links under the action of stimulating information, the path selection strategy is designed with the goal of maximizing the information transmission capacity between nodes. Four indicators are proposed: (1) pathway-synchronization-facilitation; (2) pathway-activation; (3) pathway-phase-selectivity; (4) pathway-switching-selectivity, which are used as the main basis for path selection in the network. The results show that the in-phase and anti-phase transition of neuron nodes under the action of time delay is an important way to form an effective link, and, in addition to the influence of synaptic strength and the number of central nodes on synchronization characteristics, the phase information carried by the stimulus signal also regulates the path selection. Furthermore, the paths between the pairs of stimulus nodes in the network have different phase preferences. In the brain-like network with twenty nodes, it is found that nearly 42% of the stimulus nodes have a strong phase preference; that is, the path can be selected and switched through the phase information carried by the information flow, and then the path with better representation information can be found. It also provides a new idea for how brain-like intelligences might better represent information.