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Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains

Authors
  • Nasser, Hassan
  • Cessac, Bruno
Type
Preprint
Publication Date
Apr 14, 2014
Submission Date
Apr 14, 2014
Identifiers
DOI: 10.3390/e16042244
Source
arXiv
License
Yellow
External links

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.

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