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Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
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dc.contributor.author | Endres, D M | |
dc.contributor.author | Oram, M W | |
dc.contributor.author | Schindelin, J.E. | |
dc.contributor.author | Foldiak, P | |
dc.contributor.editor | Platt, J.C. | |
dc.contributor.editor | Koller, D. | |
dc.contributor.editor | Singer, Y. | |
dc.contributor.editor | Roweis, S. | |
dc.coverage.spatial | 393-400 | en |
dc.date.accessioned | 2008-05-12T14:33:02Z | |
dc.date.available | 2008-05-12T14:33:02Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Advances in Neural Information Processing Systems 20 393-400 2008 | en |
dc.identifier.other | StAndrews.ResExp.Output.OutputID.24452 | en |
dc.identifier.uri | https://hdl.handle.net/10023/473 | |
dc.identifier.uri | http://books.nips.cc/nips20.html | |
dc.description.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. | en |
dc.format.extent | 2541 bytes | |
dc.format.extent | 295800 bytes | |
dc.format.mimetype | text/plain | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | MIT Press | en |
dc.relation.ispartof | Advances in Neural Information Processing Systems 20 | en |
dc.rights | Copyright owner Massachusetts Institute of Technology Press | |
dc.subject | bioinformatics | en |
dc.subject | Neuroscience | en |
dc.subject | Bayesian methods | en |
dc.subject | spiking neurons | en |
dc.title | Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms | en |
dc.type | Journal article | en |
dc.audience.mediator | School : Psychology | en |
dc.description.version | https://doi.org/Postprint | en |
dc.publicationstatus | Published | en |
dc.status | Peer reviewed | en |
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