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NIPS
2008

Dependent Dirichlet Process Spike Sorting

11 years 1 months ago
Dependent Dirichlet Process Spike Sorting
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.
Jan Gasthaus, Frank Wood, Dilan Görür, Y
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Jan Gasthaus, Frank Wood, Dilan Görür, Yee Whye Teh
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