Gibbs distribution analysis of temporal correlations structure in retina ganglion cells

We present a method to estimate Gibbs distributions with 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 ( Marre et al., 2009) to describe the statistics of spatio-temporal spike patterns and emphasizes the role of higher order spatio-temporal interactions.

► A method to estimate general maximum-entropy models (beyond Ising). ► A method to compare statistical models by minimizing Kullback–Leibler divergence. ► Application to analyze multi-electrode arrays spike-trains for small groups of neurons. ► For spatio-temporal patterns of two/three neurons, higher orders terms, and Markovian interactions of finite memory improve the description of the statistics.