讲座题目：Oscillatory activity and waves in neuronal networks

主讲人：Vincent Hakim 教授

主持人🏋🏻：刘宗华 教授

开始时间：2019年4月3号上午10🧔🏿‍♀️：00

讲座地址：闵行校区物理楼226报告厅

主办单位：物理与材料科学光辉平台

报告人简介◾️：

    Vincent Hakim holds a CNRS Research Director position at Ecole Normale Supérieure (ENS) in Paris, France. He is a theoretical physicist specialised in statistical physics and nonlinear dynamics. During the last twenty years, he has carried out work in theoretical neuroscience, particularly on network dynamics and oscillations. He has contributed to elucidate the mechanisms of the widespread ‘sparsely synchronized oscillations’. He has also closely collaborated with several experimental teams, particularly at ENS. This has led him to study various questions of synaptic physiology such as the observation of silent synapses in the cerebellum, the molecular mechanisms of synaptic formation and maintenance, and recently credit assignment in learning.

报告内容简介：

    Neural rhythms and collective oscillations are ubiquitously recorded in neural structures. Oscillations with 10-45Hz frequency (in the so-called « beta/low gamma » range) are thought to arise from reciprocal interactions between excitatory ( E) and inhibitory(I) neurons. Most modelling studies assume networks with random unstructured connectivities. However, several experimental results point out the need to model and analyze the spatial organization of oscillatory neuronal activity.After describing some of these data, I will describe our recent work in this direction.I will show that long-range excitation can either synchronize or desynchronize oscillatory activity of distinct E-I modules, depending on its strength and the specific considered connectivity. I will also show that stochastic action potential emission by individual neurons gives rise to noise at the module level that needs to be taken into account. As a result, the oscillatory phase dynamics in a 1D chain of E-I modules on large scales is described by the Edwards-Wilkinson, KPZ or noisy Kuramoto-Sivashinsky equations with computable coefficients. On scales relevant to the brain, the phase mode is found to be insufficient to describe the results and modes beyond the phase mode have to be taken into account.Finally, I will discuss how the results may help to explain recording data in the motor cortex during movement preparation.