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Biological Cybernetics

, Volume 88, Issue 5, pp 335–351 | Cite as

Effect of cross-trial nonstationarity on joint-spike events

  • S. Grün
  • A. Riehle
  • M. Diesmann

Abstract.

 Common to most correlation analysis techniques for neuronal spiking activity are assumptions of stationarity with respect to various parameters. However, experimental data may fail to be compatible with these assumptions. This failure can lead to falsely assigned significant outcomes. Here we study the effect of nonstationarity of spike rate across trials in a model-based approach. Using a two-rate-state model, where rates are drawn independently for trials and neurons, we show in detail that nonstationarity across trials induces apparent covariation of spike rates identified as the generator of false positives. This finding has specific implications for the ``shuffle predictor.'' Within the framework developed for our model, covariation of spike rates and the mechanism by which the shuffle predictor leads to wrong interpretation of the data can be discussed. Corrections for the influence of nonstationarity across trials by improvements of the predictor are presented.

Keywords

Experimental Data Correlation Analysis False Positive Spike Activity Spike Rate 
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 2003

Authors and Affiliations

  • S. Grün
    • 1
  • A. Riehle
    • 2
  • M. Diesmann
    • 3
  1. 1.Max-Planck-Institute for Brain Research, Department of Neurophysiology, 60528 Frankfurt, GermanyDE
  2. 2.Equipe “Perception and Cognition,” CNRS-INPC, Marseille, FranceFR
  3. 3.Department of Nonlinear Dynamics, Max-Planck-Institut für Strömungsforschung, Göttingen, GermanyDE

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