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A comparative study of pattern detection algorithm and dynamical system approach using simulated spike trains

  • Igor V. Tetko
  • Alessandro E. P. Villa
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

We apply two different approaches—pattern detection algorithm and dynamical system analysis—to study sets of simulated spike trains produced by chaotic attractors and Poisson processes. We show that both algorithms are able to detect a deterministic activity in the chaotic spike trains and they are tolerant to the presence of noise in input data. A method for noise filtering in input data series is proposed and its application is demonstrated for the simulated data sets.

Keywords

Spike Train Correlation Dimension Chaotic Attractor Chaotic Time Series Dynamical System Approach 
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 1997

Authors and Affiliations

  • Igor V. Tetko
    • 1
    • 2
  • Alessandro E. P. Villa
    • 1
  1. 1.Laboratoire de Neuro-heuristiqueInstitut de Physiologie UNILLausanneSwitzerland
  2. 2.Institute of Bioorganic and Petroleum ChemistryKiev-660Ukraine

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