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

Hassan Nasser 1 Bruno Cessac 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
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 (Dudik et al 04 and Broderick et al 07) 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.
Document type :
Reports
Complete list of metadatas

Cited literature [45 references]  Display  Hide  Download

https://hal.inria.fr/hal-00927080
Contributor : Hassan Nasser <>
Submitted on : Friday, January 10, 2014 - 6:12:05 PM
Last modification on : Thursday, May 3, 2018 - 1:32:58 PM
Long-term archiving on : Friday, April 11, 2014 - 9:50:28 AM

File

paper.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00927080, version 1

Collections

Citation

Hassan Nasser, Bruno Cessac. Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains.. [Research Report] 2014. ⟨hal-00927080⟩

Share

Metrics

Record views

736

Files downloads

257