Neuron Field Models: Beyond the Mean-Field Approximation


Yao Li, Department of Mathematics and Statistics, University of Massachusetts Amherst


2018.06.08 13:00-14:00


601 Pao Yue-Kong Library


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.