Spike Sorting: Bayesian Clustering of Non-Stationary Data

Aharon Bar-Hillel, Adam Spiro, Eran Stark

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and splits of clusters.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PublisherNeural information processing systems foundation
ISBN (Print)0262195348, 9780262195348
StatePublished - 1 Jan 2005
Externally publishedYes
Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
Duration: 13 Dec 200416 Dec 2004

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference18th Annual Conference on Neural Information Processing Systems, NIPS 2004
CityVancouver, BC

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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