Dependent Dirichlet Process Spike Sorting

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Jan Gasthaus, Frank Wood, Dilan Gorur, Yee Teh


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 the a partial ground truth labeling is known."