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We propose a numerical method to learn Maximum Entropy (MaxEnt) distributionswith spatio-temporal constraints from experimental spike trains. This is anextension of two papers [10] and [4] who proposed the estimation of parameterswhere only spatial constraints were taken into account. The extension wepropose allows to properly handle memory effects in spike statistics, for largesized neural networks.

Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains