Recent advances in multi-electrodes array acquisition has made it possible torecord the activity of up to several hundreds of neurons at the same time andto register their collective spiking activity. This opens up new perspectivesin understanding how a neuronal network encodes the response to a stimulus, andwhat a spike train tells up about the network structure and nonlinear dynamics.For this, one has to develop statistical models properly handling thespatio-temporal aspects of spike trains, including memory effects. In thistalk, I will review several such statistical models, including Maximum EntropyModels, Generalized Linear Model or neuromimetic models dealing with theiradvantages, limits, and relations.