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