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Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/473
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Title: Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
Authors: Endres, D M
Oram, M W
Schindelin, J.E.
Foldiak, P
Editors: Platt, J.C.
Koller, D.
Singer, Y.
Roweis, S.
Keywords: bioinformatics
Neuroscience
Bayesian methods
spiking neurons
Issue Date: 2008
Citation: Advances in Neural Information Processing Systems 20 393-400 2008
Abstract: 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.
Version: Postprint
URI: http://hdl.handle.net/10023/473
http://books.nips.cc/nips20.html
Type: Journal article
Rights: Copyright owner Massachusetts Institute of Technology Press
Publication Status: Published
Status: Peer reviewed
Publisher: MIT Press
Appears in Collections:Institute of Behavioural and Neural Sciences Research
Psychology & Neuroscience Research



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