Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains
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], which proposed the estimation of parameters where only spatial constraints were taken into account. The ext...
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MDPI AG
2014-04-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/16/4/2244 |
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doaj-72e252c986f546a79cc07ac36c96d4712020-11-24T20:52:27ZengMDPI AGEntropy1099-43002014-04-011642244227710.3390/e16042244e16042244Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike TrainsHassan Nasser0Bruno Cessac1INRIA, 2004 route de lucioles, 06560, Sophia-Antipolis, FranceINRIA, 2004 route de lucioles, 06560, Sophia-Antipolis, FranceWe 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], which proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows one to properly handle memory effects in spike statistics, for large-sized neural networks.http://www.mdpi.com/1099-4300/16/4/2244neural codingGibbs distributionmaximum entropyconvex dualityspatio-temporal constraintslarge-scale analysisspike trainMEA recordings |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hassan Nasser Bruno Cessac |
| spellingShingle |
Hassan Nasser Bruno Cessac Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains Entropy neural coding Gibbs distribution maximum entropy convex duality spatio-temporal constraints large-scale analysis spike train MEA recordings |
| author_facet |
Hassan Nasser Bruno Cessac |
| author_sort |
Hassan Nasser |
| title |
Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains |
| title_short |
Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains |
| title_full |
Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains |
| title_fullStr |
Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains |
| title_full_unstemmed |
Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains |
| title_sort |
parameter estimation for spatio-temporal maximum entropy distributions: application to neural spike trains |
| publisher |
MDPI AG |
| series |
Entropy |
| issn |
1099-4300 |
| publishDate |
2014-04-01 |
| description |
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], which proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows one to properly handle memory effects in spike statistics, for large-sized neural networks. |
| topic |
neural coding Gibbs distribution maximum entropy convex duality spatio-temporal constraints large-scale analysis spike train MEA recordings |
| url |
http://www.mdpi.com/1099-4300/16/4/2244 |
| work_keys_str_mv |
AT hassannasser parameterestimationforspatiotemporalmaximumentropydistributionsapplicationtoneuralspiketrains AT brunocessac parameterestimationforspatiotemporalmaximumentropydistributionsapplicationtoneuralspiketrains |
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1716799636721631232 |