Study Of Synchronization Transition Behaviors Due To Noise In Delayed Neural Network

In this paper, we numerically study the synchronization transition behaviors dueto noise in delayed neural network. It is found that the noise intensity ofthermo-sensitive neurons in time-delay neural network and the effect of the fluctuationof coupling strength could play a vital role on nonlinear dynamics behavior.We studied the effect of noise on the synchronization transition behaviors oftime-delay neural network. The study found that:First, it is found that the neurons exhibit synchronization transitions as synapticnoise strength is varied, and the synchronization transitions are enhanced when timedelay is proper. Noise can enhance synchronization transition behaviors of neuralnetw-ork due to time-delay, and under the use of noise, it also can effect thesynchroniza-tion transitions by changing coupling strength and the averageconnection degree. However, in the electrical coupling and the chemical couplingconnection modes, the effect of noise is not the same.Then, regulating the coupling strength of neurons by noise, we numerically studythe effect of the fluctuation of coupling strength on the synchronization of scale-freeneuronal network with time delays. It is found that the neurons exhibit synchronizati-ontransitions when noise intensity is varied, and the synchronization transitions aredelay-dependent and are enhanced at certain time delays. This phenomenon becomesstronger for chemical coupling connection than for electrical coupling connection. Asnetwork average degree increases, this phenomenon decreases monotonically forelectrical coupling connection. However, for chemical coupling connection there is anoptimal network average degree at which the phenomenon becomes strongest.These results show that noise can induce different synchronization transitions inthe scale-free neuronal network with electrical or chemical coupling connection, andhence it could play different roles in the information processing and informationtrans-mission of neural systems. It also can help us better understand the importance ofnoi-se in improving the precision of information transmission in neural networks.These phenomena not only expanded our vision, but also increase the theoreticalbasis of neural network dynamical behaviors. At the same time it also can be used tointerpret the information processing and transmission.