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ANN-Based System for Sorting Spike Waveforms Employing Refractory Periods

  • Thomas Hermle
  • Martin Bogdan
  • Cornelius Schwarz
  • Wolfgang Rosenstiel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

We describe a modification of a growing grid neural net for the purpose of sorting neuronal spike waveforms from extracellular recordings in the central nervous system. We make use of the fact, that real neurons exhibit a refractory period after firing an action potential during which they can not create a new one. This information is utilized to control the growth process of a growing grid, which we use to classify spike waveforms. The new algorithm is an alternative to a standard self-organizing map used in our previously published spike sorting system. Using simulated data, we show that this modification can further improve the accuracy in sorting neuronal spike waveforms.

Keywords

Spike Train Refractory Period Independent Component Analysis Multi Electrode Array Spike Sorting 
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 2005

Authors and Affiliations

  • Thomas Hermle
    • 1
    • 2
  • Martin Bogdan
    • 1
  • Cornelius Schwarz
    • 2
  • Wolfgang Rosenstiel
    • 1
  1. 1.Wilhelm-Schickard-Institut für Informatik, Technische InformatikUniversität TübingenTübingenGermany
  2. 2.Hertie-Institut für klinische Hirnforschung, Kognitive NeurologieUniversitätsklinik TübingenTübingenGermany

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