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学术报告:A Probability Polling State — the Maximum Entropy Principle in High Dimensional Neuronal Data Analysis

发布时间:2017年06月12日 浏览次数:发布者:mathky

报告人:许志钦博士

  纽约大学阿布扎比分校

报告题目:A Probability Polling State — the Maximum Entropy Principle in High Dimensional Neuronal Data Analysis

报告时间: 2017616日下午15:15

报告地点:海韵实验楼108

内容摘要:How to extract information from exponentially growing recorded neuronal data is a great scientific challenge. It is urgent to develop methods to simplify the analysis of neuronal data. In this talk, we address what kind of dynamical states of neuronal networks allows us to have an effective description of coding schemes. For asynchronous neuronal networks, when considering the probability increment of a neuron spiking induced by other neurons, we found a probability polling (p-polling) state that captures the neuronal interactions which are affected by multiple factors, i.e., coupling structure, background input and external input. We show that this state is confirmed in many experiments in vivo, and also confirmed through the simulation of Hodgkin-Huxley neuronal networks. We hypothesize that this p-polling state may be a general operating state of neuronal networks. For the p-polling state, we show that high dimensional neuronal firing patterns can be well captured by the 2 nd order maximum entropy model.

报告人简介:许志钦博士于20012和2016年在上海交通大学获学士和博士学位,从2016年至今,他在纽约大学阿布扎比分校和美国纽约大学库朗研究所从事博士后研究。许志钦博士目前的主要研究兴趣是理论和计算神经科学领域的科学问题,包括神经元网络动力学的信息编码原理的研究,针对神经生理实验现象的数学建模与动力学模拟以及神经元网络机制的研究,利用统计物理和高维统计发展有助于神经实验的数据处理方法等,其工作发表在如Phys. Rev. Lett., PLoS ONE,以及Commun. Math. Sci.等国际期刊上。

学院联系人:吴聪敏副教授

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