报告题目 ：Neuron field models: beyond the mean-field approximation
报告摘要如下：
In this talk I will present my recent result about a stochastic neural field model that aims to describe the spiking activities of a small piece of cortex. Neurons in this model have a very simple version of integrate-and-fire dynamics, which makes the model mathematically tractable. At the same time, this model can produce very rich spiking patterns ranging from time-homogeneous spiking to fully synchronized spike volleys. I will present various rigorous and numerical results related to this model, including the stochastic stability, the mean- field approximations and their discrepancies, and the spatial correlation of spike counts.
报告人信息：
Yao Li （李尧）
Assistant Professor 助理教授
Department of Mathematics and Statistics
University of Massachusetts Amherst
