Chaos Synchronization Analysis And Control Of Neurons

Biological system as one of the most complex systems has attracted great attention since several years ago and intensive researches in this field have been done. To understand the basic information processing of biological systems, especially neural systems including single neurons and neural networks, important tools have been developed in the research of neuron science. Furthermore, medical evidence shows great relevancy between the synchronization of neural systems and the information of cerebral process. Especially, great efforts have been devoted to synchronization control of neural systems because the presence, absence or degree of synchronization can be an important part of the function or dysfunction of them.In this paper, the FitzHugh-Nagumo(FHN) model, Hodgkin-Huxley(HH) model and Ghostburster model under external electric field(EEL) are chosen as our research objects. After studying the nonlinear characteristics of single neuron, the author improves FHN neurons, HH neurons and Ghostburster neurons under external electric field, respectively, as the authorll as the master-slave model and error dynamic model of each neuron accordingly. Considering the effects of EEL and uncertainty of model parameters, the author designs the controller according to the composite control law to realize the synchronization control. The author develops the composite controller based on the control methods such as active sliding-mode control and neural network control for FHN model, fuzzy H∞control and sliding-mode control based on RBFNN (Radial Basis Function neural network) for HH model and finally, H∞control based on RBFNN, adaptive control based on RBFNN and high order sliding-mode control for Ghostburster model.The detailed theoretical analysis and the numerical simulation results verify the validity and effectiveness of these proposed methods.These results provide new methods and ideas to the researches on brain and the quantitative analysis of the acupuncture.