Review Article | Published:

The mechanics of state-dependent neural correlations

Nature Neuroscience volume 19, pages 383393 (2016) | Download Citation

Abstract

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.

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Acknowledgements

This work was funded by National Science Foundation grants NSF-DMS-1313225 (B.D.), NSF-DMS-1517082 (B.D.), NIH-CRCNS R01DC015139-01ZRG1 (B.D.), NSF-DMS-1122094 (K.J.), NSF-DMS-1517629 (K.J.) and NSF-DMS-1517828 (R.R.), National Institute of Health grant NIH:1F32DC014387 (A.L.-K.), a grant from the Simons Foundation collaboration on the global brain (B.D.), and a Simons Foundation fellowship (K.J.).

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Affiliations

  1. Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

    • Brent Doiron
    • , Ashok Litwin-Kumar
    • , Robert Rosenbaum
    •  & Gabriel K Ocker
  2. Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.

    • Brent Doiron
    • , Ashok Litwin-Kumar
    • , Robert Rosenbaum
    •  & Gabriel K Ocker
  3. Center for Theoretical Neuroscience, Columbia University, New York, New York, USA.

    • Ashok Litwin-Kumar
  4. Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.

    • Robert Rosenbaum
  5. Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, USA.

    • Robert Rosenbaum
  6. Allen Institute for Brain Science, Seattle, Washington, USA.

    • Gabriel K Ocker
  7. Department of Mathematics, University of Houston, Houston, Texas, USA.

    • Krešimir Josić
  8. Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA.

    • Krešimir Josić

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The authors declare no competing financial interests.

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Correspondence to Brent Doiron.

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