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NIPS
2007

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

10 years 2 months ago
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
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 [1, 2]. 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.
Dominik Endres, Mike W. Oram, Johannes E. Schindel
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NIPS
Authors Dominik Endres, Mike W. Oram, Johannes E. Schindelin, Peter Földiák
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