Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains
Neurons and Cognition
2015-06-19 v1 Biological Physics
Abstract
We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neural networks.
Cite
@article{arxiv.1404.3470,
title = {Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains},
author = {Hassan Nasser and Bruno Cessac},
journal= {arXiv preprint arXiv:1404.3470},
year = {2015}
}
Comments
34 pages, 33 figures