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Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains
- Source :
- Entropy, Entropy, MDPI, 2014, 16 (4), pp.2244-2277. ⟨10.3390/e16042244⟩, Entropy, 2014, 16 (4), pp.2244-2277. ⟨10.3390/e16042244⟩, Entropy, Vol 16, Iss 4, Pp 2244-2277 (2014), Entropy; Volume 16; Issue 4; Pages: 2244-2277
- Publication Year :
- 2014
- Publisher :
- MDPI AG, 2014.
-
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.<br />Comment: 34 pages, 33 figures
- Subjects :
- maximum entropy
Computer science
[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS]
[PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph]
FOS: Physical sciences
General Physics and Astronomy
neural coding
lcsh:Astrophysics
01 natural sciences
spike train
spatio-temporal constraints
010305 fluids & plasmas
lcsh:QB460-466
0103 physical sciences
Gibbs distribution
convex duality
large-scale analysis
MEA recordings
Physics - Biological Physics
[PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]
lcsh:Science
010306 general physics
Quantitative Biology::Neurons and Cognition
Artificial neural network
Principle of maximum entropy
Numerical analysis
Extension (predicate logic)
lcsh:QC1-999
Biological Physics (physics.bio-ph)
Quantitative Biology - Neurons and Cognition
FOS: Biological sciences
lcsh:Q
Neurons and Cognition (q-bio.NC)
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Train
Spike (software development)
Algorithm
lcsh:Physics
Subjects
Details
- ISSN :
- 10994300
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- Entropy
- Accession number :
- edsair.doi.dedup.....1b09d970f84e3b9b4ea0c5f5e7657199