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

Dominik Maria Endres, Michael William Oram, J.E. Schindelin, Peter Foldiak

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The peristimulus time histogram (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 spike trains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin width or a kernel size is often done in an relatively arbitrary fashion, even though there have been recent attempts to remedy this situation. We develop an exact Bayesian, generative model approach to estimating PSTHs and demonstate its superiority to competing methods. Further advantages of our scheme include automatic complexity control and error bars on its predictions.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 20
EditorsJ.C. Platt, D. Koller, Y. Singer, S. Roweis
PublisherMIT Press
Publication statusPublished - 2008


  • bioinformatics
  • Neuroscience
  • Bayesian methods
  • spiking neurons


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