Analysis And Control Of The Synchronization Of Neuron Networks

Neural system is combined with different kinds of neural networks which aredifferent in the type of neurons, the topology and parameters. Experiment shows thatthere is strong correlation between synchronization and information transfer in theneural system. Synchronization among the neurons and networks is a criticalmeasurement on judging whether the function of neural system is in normalconditions or not. Over synchronization or the loss of synchronization may lead tosome neural disease in neural system, so it is important to study the relationshipbetween synchronization and parameters and topology in order to further explore theprinciple of information transfer in neural system. The main content of this paper is todiscuss the principle of synchronization from different factors of neuron networks,and study how to achieve synchronization in neural networks with the help of controltheory.First, through comparing the synchronization of34networks with differenttopologies, we find that the self-coupling may make the network difficult to achievesynchronization, and prove it through the stability theory of synchronization error.Then we study the effect of time-delayed coupling on synchronization in theaspect of the effect of inner and outer parameters of neurons. We find that thetime-delayed coupling may enhance the synchronization of network in someconditions. The simulation results prove that this phenomenon is caused by theelimination of chaos in the neurons via time-delayed coupling.Finally, we use control theory to estimate and control an ill-network withunknown parameters and topology back to normal condition by adjusting neurons andsynapses parameters in the network based on adaptive theory and learning law, whichprovide a new potential method for curing mental disease.