Probabilistic Models of Neuronal Spike Trains
This paper looks et time series associated with neuronal signals. It provides a brief overview of some of the main problems and applications in this area, and develops a general probabilistic approach for the study of spike trains. Here we first motivate the problematic by looking specifically at two of the main techniques used by neuroscientists : multi unit recording, and compartmental modeling. We then briefly review the genaral Bayesian framework for inference and probabilistic modeling. Probabilistic models of spike trains of increasing complexity are introduced and demonstrated in the following sections.