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.
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Received: 14 February 2002 / Accepted: 26 November 2002 / Published online: 8 May 2003
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ID="*" Present address: Free University Berlin, Institute for Biology, Neurobiology, Berlin, Germany
Correspondence to: S. Grün (e-mail: gruen@neurobiologie.fu-berlin.de, Tel.: +49-30-83856635, Fax: +49-30-83855455)
Acknowledgements. We thank George Gerstein, Günther Palm, Gordon Pipa, Stefan Rotter, and Gaby Schneider for stimulating discussions and Denny Fliegner for advice on computational methods. Supported in part by the Volkswagen Foundation (SG). Part of the computations were performed using the parallel computing facilities at the Max-Planck-Institut für Strömungsforschung.
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Grün, S., Riehle, A. & Diesmann, M. Effect of cross-trial nonstationarity on joint-spike events. Biol. Cybern. 88, 335–351 (2003). https://doi.org/10.1007/s00422-002-0386-2
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DOI: https://doi.org/10.1007/s00422-002-0386-2