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BC
Bruno Cessac
12
Documents
Présentation
Publications
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Exact computation of the Maximum Entropy Potential of spiking neural networksmodelsPhysical Review E , 2014, 89 (052117), pp.13
Article dans une revue
hal-01095599v1
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Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike TrainsEntropy, 2014, 16 (4), pp.2244-2277. ⟨10.3390/e16042244⟩
Article dans une revue
hal-01096213v1
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Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational Properties of Biological Recurrent Neural NetworksLACONEU 2014, Jan 2014, Valparaiso, Chile. 1 page
Communication dans un congrès
hal-01095601v1
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Neural Networks DynamicsLACONEU 2014, Jan 2014, Valparaiso, Chile
Communication dans un congrès
hal-01095600v1
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Spike train statistics: from mathematical models to software to experiments6th Workshop in Computational Neuroscience in Marseille, Mar 2014, Marseille, France
Communication dans un congrès
hal-01095746v1
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Analyzing large-scale spike trains data with spatio-temporal constraintsNeuroComp/KEOpS'12 workshop beyond the retina: from computational models to outcomes in bioengineering. Focus on architecture and dynamics sustaining information flows in the visuomotor system., Oct 2012, Bordeaux, France
Communication dans un congrès
hal-00756467v1
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ENAS: A new software for spike train analysis and simulationPoster de conférence hal-01368757v1 |
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Can We Hear the Shape of a Maximum Entropy Potential From Spike Trains?Poster de conférence hal-01095760v1 |
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From Habitat to Retina: Neural Population Coding using Natural MoviesPoster de conférence hal-01095781v1 |
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Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapsesAREADNE 2012. Encoding And Decoding of Neural Ensembles, Jun 2012, Santorini, Greece. 2012
Poster de conférence
hal-00850107v1
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PRANAS: A new platform for retinal analysis and simulation[Research Report] RR-8958, Inria Sophia Antipolis; Inria Bordeaux Sud-Ouest. 2017, pp.27
Rapport
hal-01377307v2
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Parametric Estimation of Gibbs distributions as generalized maximum-entropy models for the analysis of spike train statistics.[Research Report] RR-7561, INRIA. 2011, pp.54
Rapport
inria-00574954v2
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