Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns inneural networks is a central question in computational neuroscience. Maximum Entropy approachhas been successfully used to characterize the statistical response of simultaneously recorded spikingneurons responding to stimuli. But, in spite of good performance in terms of prediction, the ttingparameters do not explain the underlying mechanistic causes of the observed correlations. On theother hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilisticmapping between stimulus, network architecture and spike patterns in terms of conditional proba-bilities. In this paper we build an exact analytical mapping between neuro-mimetic and MaximumEntropy models.