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Scaled correlation analysis: a better way to compute a cross-correlogram

Authors

  • Danko Nikolić,

    1. Max Planck Institute for Brain Research, Frankfurt am Main, Germany
    2. Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
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  • Raul C. Mureşan,

    1. Max Planck Institute for Brain Research, Frankfurt am Main, Germany
    2. Center for Cognitive and Neural Studies, Romanian Institute of Science and Technology, Cluj-Napoca, Romania
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  • Weijia Feng,

    1. Max Planck Institute for Brain Research, Frankfurt am Main, Germany
    2. Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
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  • Wolf Singer

    1. Max Planck Institute for Brain Research, Frankfurt am Main, Germany
    2. Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
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Danko Nikolić, 1Max Planck Institute for Brain Research, as above.
E-mail: danko.nikolic@gmail.com

Abstract

When computing a cross-correlation histogram, slower signal components can hinder the detection of faster components, which are often in the research focus. For example, precise neuronal synchronization often co-occurs with slow co-variation in neuronal rate responses. Here we present a method – dubbed scaled correlation analysis – that enables the isolation of the cross-correlation histogram of fast signal components. The method computes correlations only on small temporal scales (i.e. on short segments of signals such as 25 ms), resulting in the removal of correlation components slower than those defined by the scale. Scaled correlation analysis has several advantages over traditional filtering approaches based on computations in the frequency domain. Among its other applications, as we show on data from cat visual cortex, the method can assist the studies of precise neuronal synchronization.

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