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Pré-Publication, Document De Travail Année : 2014

Hearing the Maximum Entropy Potential of neuronal networks

Rodrigo Cofre
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Bruno Cessac

Résumé

We consider a spike-generating stationary Markov process whose transition probabilities are known. We show that there is a canonical potential whose Gibbs distribution, obtained from the Maximum Entropy Principle (MaxEnt), is the equilibrium distribution of this process. We provide a method to compute explicitly and exactly this potential as a linear combination of spatio-temporal interactions. In particular, our results establish an explicit relation between Maximum Entropy models and neuro-mimetic models used in spike train statistics.
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Dates et 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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Rodrigo Cofre, Bruno Cessac. Hearing the Maximum Entropy Potential of neuronal networks. 2014. ⟨hal-00861397v3⟩
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