Pattern-stimulation and large-scale activity imaging both require methods for synthetically generating and analyzing multiple spike trains, taking into account features like their correlation structure. We have introduced a strategy based on correlation distortions in the Linear-Nonlinear-Poisson (LNP) model for generating multiple spike trains with an exactly controlled correlation structure, and for identifying neural encoding models and directed information flow in networks of neurons.
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