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Preprints, Working Papers, ... Year : 2014

Exact computation of the Maximum Entropy Potential of spiking neural networks models

Rodrigo Cofre
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  • PersonId : 943307
Bruno Cessac

Abstract

Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional probabilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.
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Dates and versions

hal-00861397 , version 1 (12-09-2013)
hal-00861397 , version 2 (20-09-2013)
hal-00861397 , version 3 (06-01-2014)
hal-00861397 , version 4 (14-05-2014)

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  • HAL Id : hal-00861397 , version 4

Cite

Rodrigo Cofre, Bruno Cessac. Exact computation of the Maximum Entropy Potential of spiking neural networks models. 2014. ⟨hal-00861397v4⟩
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