PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dependent Dirichlet Process Spike Sorting
Jan Gasthaus, Frank Wood, Dilan Gorur and Yee Whye Teh
In: Advances in Neural Information Processing Systems 21 (2009) NIPS Foundation , pp. 497-504.


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:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:4676
Deposited By:Jan Gasthaus
Deposited On:24 March 2009