Small World Neuron Network Synchronization Control And Statistical Rules

The body's nervous system is a very complex, large, efficient operation of the network system, which transmit information fast and accurate control system is unmatched by any. In recent years, as technological development, computer science, information science, biomedical, control the fields of science researchers to study different aspects of the working mechanism of neural network, modeling the process and it deals with information to deal with neurological diseases. A large number of previous studies, this study method, by studying the complex network approach to nonlinear system analysis, the efficiency of small world neural network, mathematical statistics theory, neural network model and its parameters adjusted to achieve synchronization by calculating a large number of neuronal synchronization interval, according to statistical theory, neural network obtained the release of statistical regularity. These studies not only the development of the neural network has a positive meaning, while giving such as epilepsy and Parkinson's disease and other nervous system to provide some medical reference, and also some foreign biological neural systems to provide some theoretical basis for experiments.The main work of this article:First, neural network model of learning and parameter adjustment Neuron model by comparison of the various select the appropriate model to study the problem, we select the FN model. In order to achieve synchronous coupling parameter adjustment were to adopt a different neural network input to simulate the real electrical activity of neurons in the network model under different parameters showed different release characteristics.Second, the study of complex networks and small world neural network research and applied FN neural network control system. Previous studies by learning the methods of complex networks, focusing on small world network simplification and simulation of neural systems, the nonlinear system, simplify the processing methods studied, the preparation program to simulate the FN neural network, the synchronization process, adjust coupling parameters, see the difference result of synchronization from different Control stimulus input, analog neural networks with different stimuli synchronized effect. Synchronized graphics data obtained by calculation, and calculation of all cases, the release of neural network interval.Third, according to previous results, the use of data processing methods and statistics, law, analyzing the distribution of neural network statistics, drawn neurons in resting and nervous system under different input characteristics of the distribution law, and coupling parameters in different neurons rules issued by contrast of different parameters on the impact of network synchronization. Our results consistent with overseas experiments results broadly.The study of the neural mechanism of information transfer be helpful, while treatment of the disease to the nervous system to provide some theoretical basis.