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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Jan Gasthaus, Frank Wood, Dilan Görür, Yee Whye Teh
• Keywords: Action Potential Waveform, Information Technology, NIPS 2008, Potential Waveform Drift, Refractory Period Violations