PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dependent Dirichlet process spike sorting
Jan Gasthaus, Frank Wood, Dilan Gorur and Yee Whye Teh
In: NIPS 2008, 8-11 Dec 2008, Vancouver, Canada.


In this paper we propose a new incremental spike sorting model that automatically eliminates refractory period violations, accounts for action potential waveform drift, and can handle “appearance” and “disappearance” of neurons. Our approach is to augment a known time-varying Dirichlet process that ties together a sequence of infinite Gaussian mixture models, one per action potential waveform observation, with an interspike-interval-dependent likelihood that prohibits refractory period violations. We demonstrate this model by showing results from sorting two publicly available neural data recordings for which a partial ground truth labeling is known.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4864
Deposited By:Dilan Gorur
Deposited On:24 March 2009