Neuronal firing patterns are related to the information processing in neural system.In this paper,we investiga...
WANG Hai-Yang1,2,WANG Jiang1,CHE Yan-Qiu1,HAN Chun-Xiao1,LI Hong-Li1,CHEN Ying-Yuan11.School of Electrical Engineering and Automation,Tianjin University,Tianjin 300072,P.R.China2.Department of mechanical and Electrical Engineering,Baicheng Normal College,Jilin,Baicheng 137000,P.R.China
＜正＞This paper proposes a particle swarm optimization（PSO） to identify the optimal parameter set of periodic de...
CHEN Yingyuan~1,WANG Jiang~1,WEI Xile~1,DENG Bin~1,CHE Yanqiu~2 1.School of Electrical and Automation Eng.,Tianjin University,Tianjin 300072,P.R.China 2.Tianjin Key Laboratory of Information Sensing & Intelligent Control,Tianjin University of Technology and Education, Tianjin 300072,P.R.China
Neuronal networks in the brain exhibit the modular （clustered） property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intra coupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network, Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.
