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arXiv · 1309.5873

Hearing the Maximum Entropy Potential of neuronal networks

Rodrigo Cofre·Bruno Cessac
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Abstract

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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Rodrigo Cofre, Bruno Cessac. 2014-01-06. Hearing the Maximum Entropy Potential of neuronal networks. https://arxiv.org/abs/1309.5873

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