  We provide rigorous and exact results characterizing the statistics of spike
trains in a network of leaky integrate and fire neurons, where time is discrete
and where neurons are submitted to noise, without restriction on the synaptic
weights. We show the existence and uniqueness of an invariant measure of Gibbs
type and discuss its properties. We also discuss Markovian approximations and
relate them to the approaches currently used in computational neuroscience to
analyse experimental spike trains statistics.
