title: A non-parametric Bayesian approach to spike sorting.
abstract: 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.
authors.affiliation: Department of Computer Science, Brown University, Providence, RI, USA. fwood@cs.brown.edu
authors.forename: Frank
authors.initials: F
authors.lastname: Wood
authors.forename: Sharon
authors.initials: S
authors.lastname: Goldwater
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Black
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D000882:Haplorhini; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*