Bifurcation and synchronization of synaptically coupled FHN models with time delay |
| |
| Authors: | Qingyun Wang Qishao Lu GuanRong Chen Zhaosheng feng LiXia Duan |
| |
| Institution: | 1. Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China;2. School of Mathematics and Statistics, Shandong Normal University, Ji′nan, 250014, China;3. School of Science, Chongqing University of Posts and Telecommunications, Chongqing 430065, China;1. Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China;2. School of Science, Chongqing University of Posts and Telecommunications, Chongqing 430065, China;3. NAAM-Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O.Box 80203, Jeddah 21589, Saudi Arabia;4. Quaid I Azam Univ, Dept Math, Islamabad 44000, Pakistan;1. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou,730070,China;2. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;1. Institute of Artificial Intelligence, School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China |
| |
| Abstract: | This paper presents an investigation of dynamics of the coupled nonidentical FHN models with synaptic connection, which can exhibit rich bifurcation behavior with variation of the coupling strength. With the time delay being introduced, the coupled neurons may display a transition from the original chaotic motions to periodic ones, which is accompanied by complex bifurcation scenario. At the same time, synchronization of the coupled neurons is studied in terms of their mean frequencies. We also find that the small time delay can induce new period windows with the coupling strength increasing. Moreover, it is found that synchronization of the coupled neurons can be achieved in some parameter ranges and related to their bifurcation transition. Bifurcation diagrams are obtained numerically or analytically from the mathematical model and the parameter regions of different behavior are clarified. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
