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3 results on '"large-scale analysis"'

1. Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains

Author

Hassan Nasser and Bruno Cessac

Subjects
neural coding, Gibbs distribution, maximum entropy, convex duality, spatio-temporal constraints, large-scale analysis, spike train, MEA recordings, Science, Astrophysics, QB460-466, Physics, QC1-999
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], 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.

Published
2014
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2. Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains.

Author

Nasser, Hassan and Cessac, Bruno

Subjects
*MAXIMUM entropy method, *STATISTICAL models, *MONTE Carlo method, *STATISTICAL mechanics, *NEURONS
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], 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. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
View/download PDF

3. Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains

Author

Bruno Cessac, Hassan Nasser, Mathematical and Computational Neuroscience (NEUROMATHCOMP), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Alexandre Dieudonné (JAD), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), ANR-10-INTB-0204,KEOpS,Algorithmes pour la modélisation du système visuel: de la vision naturelle aux applications numériques.(2010), European Project: 600847,EC:FP7:ICT,FP7-ICT-2011-9,RENVISION(2013), European Project: 318723,EC:FP7:ICT,FP7-ICT-2011-8,MATHEMACS(2012), European Project: 269921,EC:FP7:ICT,FP7-ICT-2009-6,BRAINSCALES(2011), European Project: 227747,EC:FP7:ERC,ERC-2008-AdG,NERVI(2009), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)

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
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., Comment: 34 pages, 33 figures

Published
2014

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