Bayesian correction for attenuation of correlation in multi-trial spike count data.

Journal of Neurophysiology
Sam BehsetaRobert E Kass

Abstract

When correlation is measured in the presence of noise, its value is decreased. In single-neuron recording experiments, for example, the correlation of selectivity indices in a pair of tasks may be assessed across neurons, but, because the number of trials is limited, the measured index values for each neuron will be noisy. This attenuates the correlation. A correction for such attenuation was proposed by Spearman more than 100 yr ago, and more recent work has shown how confidence intervals may be constructed to supplement the correction. In this paper, we propose an alternative Bayesian correction. A simulation study shows that this approach can be far superior to Spearman's, both in accuracy of the correction and in coverage of the resulting confidence intervals. We demonstrate the usefulness of this technology by applying it to a set of data obtained from the frontal cortex of a macaque monkey while performing serial order and variable reward saccade tasks. There the correction results in a substantial increase in the correlation across neurons in the two tasks.

References

Aug 9, 2003·Journal of Neurophysiology·Robin EdwardsDavid Perrett
May 20, 2006·Biometrical Journal. Biometrische Zeitschrift·Stephen Senn

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Citations

Dec 9, 2015·Journal of Neuroscience Methods·Michael W ColeAlan Anticevic
Mar 5, 2016·Neural Computation·Giuseppe VinciRobert E Kass
Sep 8, 2018·Journal of Computational Neuroscience·Giuseppe VinciRobert E Kass
Jun 1, 2018·The Annals of Applied Statistics·Giuseppe VinciRobert E Kass

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