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Understanding the dynamics of neural networks is a major challenge inexperimental neuroscience. For that purpose, a modelling of the recordedactivity that reproduces the main statistics of the data is required. In afirst part, we present a review on recent results dealing with spike trainstatistics analysis using maximum entropy models (MaxEnt). Most of thesestudies have been focusing on modelling synchronous spike patterns, leavingaside the temporal dynamics of the neural activity. However, the maximumentropy principle can be generalized to the temporal case, leading to Markovianmodels where memory effects and time correlations in the dynamics are properlytaken into account. In a second part, we present a new method based onMonte-Carlo sampling which is suited for the fitting of large-scalespatio-temporal MaxEnt models. The formalism and the tools presented here willbe essential to fit MaxEnt spatio-temporal models to large neural ensembles.

Spatio-temporal spike trains analysis for large scale networks using maximum entropy principle and Monte-Carlo method