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Influence Of Coupling And Noise On Oscillatory Solutions Of Neuron Models

發布者:文明辦發布時間:2019-03-20瀏覽次數:10


主講人:Jianzhong Su  Professor & Chair of Mathematics,University of Texas at Arlington


時間:2019年3月25日14:00


地點:徐匯校區數學系3號樓332報告廳


舉辦單位:數理學院


主講人簡介:

Dr. Jianzhong Su, Professor and Chair of Mathematics at University of Texas at Arlington, is an applied mathematician with expertise in computational neuroscience and partial differential equations. His recent focus is inverse problems with applications in optical tomography and EEG source reconstructions and brain dynamics. He has collaborated extensively with other neuroscientists, engineers and medical doctors on medical imaging, neuroscience, medical implants and related areas. He has published systematically on Diffuse Optical Tomography, neuron synaptic transmission, neuron electric activations (bursting) and their modeling and simulations. He is an experienced researcher, has served as PI/co-PI on over $10 million federal research, education and training funding from National Science Foundation, National Institutes of Health, US Department of Education and other agencies over the last 25 years, published over 70 peer-reviewed journal papers and been invited to 45 seminars and conferences.


內容簡介:

Neurons often exhibit bursting oscillations, as a mechanism to modulate and set pace for other brain functionalities. These bursting oscillations are distinctly characterized by a silent phase of slowly evolving steady states and an active phase of rapid firing oscillations. These bursting neurons are modeled by fast-slow systems consisting of several ordinary differential equations. In a network of neurons, their collective oscillatory behavior may differ from individual neurons due to coupling and noisy inputs from other neurons. We analyze the transition mechanisms between periodic and chaotic/random behavior in a coupled system of neurons, in two typical examples of neuron models. Using geometric and bifurcation analysis, we provide insight how coupling can regularize chaotic trajectories in one example, and how noise can benefit the network synchrony by modulating the bursting frequency in another case.


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