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Modelling Spikes with Mixtures of Factor Analysers

  • Dilan Görür
  • Carl Edward Rasmussen
  • Andreas S. Tolias
  • Fabian Sinz
  • Nikos K. Logothetis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging problem. We consider the spike sorting problem using a generative model, mixtures of factor analysers, which concurrently performs clustering and feature extraction. The most important advantage of this method is that it quantifies the certainty with which the spikes are classified. This can be used as a means for evaluating the quality of clustering and therefore spike isolation. Using this method, nearly simultaneously occurring spikes can also be modelled which is a hard task for many of the spike sorting methods. Furthermore, modelling the data with a generative model allows us to generate simulated data.

Keywords

Dimensionality Reduction Gaussian Mixture Model Factor Analysis Model Manual Cluster Spike Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dilan Görür
    • 1
  • Carl Edward Rasmussen
    • 1
  • Andreas S. Tolias
    • 1
  • Fabian Sinz
    • 1
  • Nikos K. Logothetis
    • 1
  1. 1.Max Planck Institute for Biological CyberneticsTübingenGermany

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