Partial cross-correlation analysis resolves ambiguity in the encoding of multiple movement features

Eran Stark, Rotem Drori, Moshe Abeles

    Research output: Contribution to journalArticlepeer-review

    36 Scopus citations

    Abstract

    A classical question in neuroscience is which features of a stimulus or of an action are represented in brain activity. When several features are interdependent either at a given point in time or at distinct points in time, neural activity related to one feature appears to be correlated with other features. Thus techniques that simultaneously consider multiple features cannot account for delayed interdependencies between features. The result is an ambiguity with respect to the encoded features. Here, we resolve this ambiguity by applying a novel statistical method based on partial cross-correlations. The method yields estimates of linear correlations between neural activity and a given feature that are not affected by linear correlations with other features at multiple time delays. The method also provides a graphical output measured on a scale that allows for comparisons between different features, neurons, and experiments. We use real movement data and neural activity simulated according to a wide range of tuning models to illustrate the method. When applied to real neural activity, the procedure yields results that indicate which of the considered features the neural activity is related to and at what time delays.

    Original languageEnglish
    Pages (from-to)1966-1975
    Number of pages10
    JournalJournal of Neurophysiology
    Volume95
    Issue number3
    DOIs
    StatePublished - Mar 2006

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