Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms

The peristimulus time historgram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is usually obtained by binning spiketrains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin with or a kernel size is often done in an relatively arbitrary fashion, even though there have been recent attempts to remedy this situation \cite{ShimazakiBinningNIPS2006,ShimazakiBinningNECO2007}. We develop an exact Bayesian, generative model approach to estimating PSHTs and demonstate its superiority to competing methods. Further advantages of our scheme include automatic complexity control and error bars on its predictions.