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Understanding how stimuli and synaptic connectivity in uence the statisticsof spike patterns in neural networks is a central question in computationalneuroscience. Maximum Entropy approach has been successfully used tocharacterize the statistical response of simultaneously recorded spikingneurons responding to stimuli. But, in spite of good performance in terms ofprediction, the fitting parameters do not explain the underlying mechanisticcauses of the observed correlations. On the other hand, mathematical models ofspiking neurons (neuro-mimetic models) provide a probabilistic mapping betweenstimulus, network architecture and spike patterns in terms of conditionalproba- bilities. In this paper we build an exact analytical mapping betweenneuro-mimetic and Maximum Entropy models.

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