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Measuring and interpreting neuronal correlations

Nature Neuroscience volume 14, pages 811–819 (2011)Cite this article

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Abstract

Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the effect of correlations on cortical processing.

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Figure 1: Types of pair-wise neuronal correlations.
Figure 2: Measured correlations are small when responses are weak.
Figure 3: Counting spikes over short response windows can decrease measured correlations.
Figure 4: Measured correlations grow slowly with the number of units that contribute to multiunit activity.
Figure 5: Spike sorting errors can reduce the strength of measured correlations.
Figure 6: Differences in mean rates duration predict much of the variability in rSC measurements across studies.

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Change history

  • 07 November 2011

    In the version of this article initially published, the firing rate and mean correlation value given in Table 1 for ref. 26 were incorrect. The correct values are 3.4 Hz and 0.18, respectively. This change affects the corresponding data point in Figure 6 and a value derived from this figure that was stated in the figure legend and main text, which should read "differences in mean rate can account for 33% of the across-study variance in reported values of rSC." The errors have been corrected in the PDF and HTML versions of this article.

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Acknowledgements

We are grateful to P. Latham for analytical descriptions for our simulations and for the derivation of the relationship between measurement window and correlations that are not based on common spikes and to R. Coen-Cagli for the derivation relating correlation to the proportion of spikes discarded during spike sorting. We thank R. Coen-Cagli, B. Cumming, M. Histed, X. Jia, K. Josic, J. Maunsell, A. Ni, H. Nienborg, A. Pouget, O. Schwartz, S. Tanabe, and J.A. Movshon and E. Simoncelli and members of their laboratories for helpful discussions and comments on an earlier version of the manuscript. This work was supported by US National Institutes of Health grants R01 EY016774 (A.K.) and K99 EY020844-01 (M.R.C.).

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  1. Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA

    Marlene R Cohen

  2. Department of Neuroscience and Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, USA

    Adam Kohn

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Cohen, M., Kohn, A. Measuring and interpreting neuronal correlations. Nat Neurosci 14, 811–819 (2011). https://doi.org/10.1038/nn.2842

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