A Non-Parametric Bayesian Approach to Spike Sorting

F. Wood, S. Goldwater, M.J. Black

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


In this work we present and apply infinite Gaussian mixture modeling, a non-parametric Bayesian method, to the problem of spike sorting. As this approach is Bayesian, it allows us to integrate prior knowledge about the problem in a principled way. Because it is non-parametric we are able to avoid model selection, a difficult problem that most current spike sorting methods do not address. We compare this approach to using penalized log likelihood to select the best from multiple finite mixture models trained by expectation maximization. We show favorable offline sorting results on real data and discuss ways to extend our model to online applications
Original languageEnglish
Title of host publicationEngineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)1-4244-003303
ISBN (Print)1-4244-0032-5
Publication statusPublished - 2006


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