  We present a method to estimate Gibbs distributions with
\textit{spatio-temporal} constraints on spike trains statistics. We apply this
method to spike trains recorded from ganglion cells of the salamander retina,
in response to natural movies. Our analysis, restricted to a few neurons,
performs more accurately than pairwise synchronization models (Ising) or the
1-time step Markov models (\cite{marre-boustani-etal:09}) to describe the
statistics of spatio-temporal spike patterns and emphasizes the role of higher
order spatio-temporal interactions.
