Statistics of spike trains in conductance-based neural networks: Rigorous results
Mathematical Physics
2015-03-19 v2 Dynamical Systems
math.MP
Biological Physics
Neurons and Cognition
Abstract
We consider a conductance based neural network inspired by the generalized Integrate and Fire model introduced by Rudolph and Destexhe. We show the existence and uniqueness of a unique Gibbs distribution characterizing spike train statistics. The corresponding Gibbs potential is explicitly computed. These results hold in presence of a time-dependent stimulus and apply therefore to non-stationary dynamics.
Keywords
Cite
@article{arxiv.1104.3795,
title = {Statistics of spike trains in conductance-based neural networks: Rigorous results},
author = {B. Cessac},
journal= {arXiv preprint arXiv:1104.3795},
year = {2015}
}
Comments
42 pages, 1 figure, to appear in Journal of Mathematical Neuroscience