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Static and dynamic models for spike train analysis Models, model diagnostics and open-source software

Abstract We present a collection of modeling tools for the analysis of neuronal spike trains as point processes. The conditional intensity can be modeled to depend on variables such as time elapsed since the last spike, time elapsed since the onset of stimulus, etc., and the models can be estimated nonparametrically by penalized likelihood Poisson regression or logistic regression. Model diagnostics for lack of fit are also available via timetransformed spike trains. The tools are implemented in a suite of open-source software, and are illustrated through the analysis of some spike train data collected