Microscopic approach of a time elapsed neural model
Neural and Evolutionary Computing
2015-06-09 v1
Abstract
The spike trains are the main components of the information processing in the brain. To model spike trains several point processes have been investigated in the literature. And more macroscopic approaches have also been studied, using partial differential equation models. The main aim of the present article is to build a bridge between several point processes models (Poisson, Wold, Hawkes) that have been proved to statistically fit real spike trains data and age-structured partial differential equations as introduced by Pakdaman, Perthame and Salort.
Keywords
Cite
@article{arxiv.1506.02361,
title = {Microscopic approach of a time elapsed neural model},
author = {Julien Chevallier and Maria J. Caceres and Marie Doumic and Patricia Reynaud-Bouret},
journal= {arXiv preprint arXiv:1506.02361},
year = {2015}
}