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Effect of cross-trial nonstationarity on joint-spike events.
Grun S, Riehle A, Diesmann M.
Max-Planck-Institute
for Brain Research, Department of Neurophysiology, 60528 Frankfurt,
Germany. gruen@neurobiologie.fu-berlin.de
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.
PMID: 12750896 [PubMed - indexed for MEDLINE]
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