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On-Line Real-Time Oriented Application for Neuronal Spike Sorting with Unsupervised Learning

  • Yoshiyuki Asai
  • Tetyana I. Aksenova
  • Alessandro E. P. Villa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

Multisite electrophysiological recordings have become a standard tool for exploring brain functions. These techniques point out the necessity of fast and reliable unsupervised spike sorting. We present an algorithm that performs on-line real-time spike sorting for data streaming from a data acquisition board or in off-line mode from a WAV formatted file. Spike shapes are represented in a phase space according to the first and second derivatives of the signal trace. The output of the application is spike data format file in which the timing of spike occurrences are recorded by their inter-spike-intervals. It allows its application to the study of neuronal activity patterns in clinically recorded data.

Keywords

Deep Brain Stimulation Subthalamic Nucleus Data Acquisition Board Neural Spike 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

  • Yoshiyuki Asai
    • 1
    • 2
    • 3
  • Tetyana I. Aksenova
    • 4
    • 5
  • Alessandro E. P. Villa
    • 1
    • 2
    • 4
    • 6
  1. 1.Institute for Scientific Interchange FoundationTorinoItaly
  2. 2.Neuroheuristic research group, INFORGE-CP1University of LausanneSwitzerland
  3. 3.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan
  4. 4.Laboratory of Preclinical Neuroscience, INSERM U318GrenobleFrance
  5. 5.Institute of Applied System AnalysisUkrainian Academy of SciencesUkraine
  6. 6.Laboratoire de NeurobiophysiqueUniversité Joseph Fourier GrenobleFrance

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