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

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Title: Scaled correlation analysis: a better way to compute a cross-correlogram.
Authors: Nikolić, Danko (AUTHOR), Mureşan, Raul C. (AUTHOR), Feng, Weijia (AUTHOR), Singer, Wolf (AUTHOR)
Source: European Journal of Neuroscience. Mar2012, Vol. 35 Issue 5, p742-762. 21p. 2 Diagrams, 3 Charts, 8 Graphs.
Subjects: Statistical correlation, Visual cortex, Synchronization, Motor neurons, Brain physiology
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. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Scaled correlation analysis: a better way to compute a cross-correlogram.
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  Data: <searchLink fieldCode="AR" term="%22Nikolić%2C+Danko%22">Nikolić, Danko</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mureşan%2C+Raul+C%2E%22">Mureşan, Raul C.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Weijia%22">Feng, Weijia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singer%2C+Wolf%22">Singer, Wolf</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neuroscience%22">European Journal of Neuroscience</searchLink>. Mar2012, Vol. 35 Issue 5, p742-762. 21p. 2 Diagrams, 3 Charts, 8 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+cortex%22">Visual cortex</searchLink><br /><searchLink fieldCode="DE" term="%22Synchronization%22">Synchronization</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+neurons%22">Motor neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+physiology%22">Brain physiology</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Synchronization
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              Text: Mar2012
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