Phase Synchronization Of Complex Brain Networks Of The Nervous System

The human brain is the dominant command and information hub of peopleengaged in various activities. The nervous system has many important functionssuch as receiving exteroceptive stimulation, generating signals, processing,transmitting and integrating various signals, advanced cognitive activities andmotion control. The message handling and transferring mechanism of brain networknervous system based on nervous system dynamics and complex network emerges inrecent years, this method can explain the cognitive behavior of brain nervousnetwork better. Research shows that the real brain neural network system hasobvious characteristics of small world network. With further research, it has beenfound that the real brain nervous system also has scale-free character. This thesisbuilds the small world nervous system network model and scale-free network modelwith Hodgkin-Huxley model reflecting neuronal discharge as neuron nodes, thenresearch the phase synchronization of these two different brain nervous systemcomplex network models.Firstly, this thesis introduces basic theory about nervous system dynamics andcomplex network, describes several common mathematics models in nervous systemdynamics, and introduces the stochastic resonance, coherence resonance and nervoussystem synchronous dynamics, which are the main research direction in nervoussystem dynamics.Secondly, according to the HH neuron model of nervous system dynamicsmathematics models, this thesis builds a WS small world nervous system networkbased on complex network theory, and researches the phase synchronization of thismodel. Set membrane voltage as state variable, then definite the phase of the smallworld nervous system network by analytic signal approximation, the state of phasesynchronization can be judged by computing average frequency. According to thesimulation results, the phase synchronization of small world nervous network hasclose relation with coupling degree and rewiring probability of the network.Finally, this thesis builds a BA scale-free nervous network and researches itsphase synchronization combining the complex network theory and HH neuron model.The simulation results show that when the coupling degree beyond the critical value,the phase synchronization will appear in the network. Meanwhile, in the process of constructing scale-free nervous system network, adding a new neuron nodeconnecting to other nodes each time, the number of edges m determines the networksynchronization performance, bigger m shows better network synchronizationperformance.