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Structure in neural population recordings: an expected byproduct of simpler phenomena?

Nature Neuroscience volume 20, pages 13101318 (2017) | Download Citation

Abstract

Neuroscientists increasingly analyze the joint activity of multineuron recordings to identify population-level structures believed to be significant and scientifically novel. Claims of significant population structure support hypotheses in many brain areas. However, these claims require first investigating the possibility that the population structure in question is an expected byproduct of simpler features known to exist in data. Classically, this critical examination can be either intuited or addressed with conventional controls. However, these approaches fail when considering population data, raising concerns about the scientific merit of population-level studies. Here we develop a framework to test the novelty of population-level findings against simpler features such as correlations across times, neurons and conditions. We apply this framework to test two recent population findings in prefrontal and motor cortices, providing essential context to those studies. More broadly, the methodologies we introduce provide a general neural population control for many population-level hypotheses.

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Acknowledgements

We thank the laboratories of L. Paninski, M. Churchland and K. Shenoy for discussions. We thank M. Churchland, L. Abbott and K. Miller for comments on the manuscript. We thank D. Kobak and C. Machens for discussions about and assistance with the dPCA algorithm. We thank M. Kaufman for help with Figure 1a. We thank M. Churchland, M. Kaufman, S. Ryu and K. Shenoy for the motor cortex data. We thank R. Romo and C. Brody for the prefrontal cortex data, downloaded from the CRCNS (available at the time of publication at https://crcns.org/data-sets/pfc/pfc-4). We thank T. Requarth for comments on the manuscript. We thank the 2016 Modeling Neural Activity conference for discussions and for a travel grant to GFE (MH 064537, NSF-DMS 1612914 and the Burroughs-Wellcome Fund). This work was funded by NIH CRCNS R01 NS100066-01, the Sloan Research Fellowship, the McKnight Fellowship, the Simons Collaboration on the Global Brain SCGB325233, the Grossman Center for the Statistics of Mind, the Center for Theoretical Neuroscience, the Gatsby Charitable Trust and the Zuckerman Mind Brain Behavior Institute.

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Affiliations

  1. Center for Theoretical Neuroscience, Columbia University, New York, New York, USA.

    • Gamaleldin F Elsayed
    •  & John P Cunningham
  2. Department of Neuroscience, Columbia University Medical Center, New York, New York, USA.

    • Gamaleldin F Elsayed
  3. Grossman Center for the Statistics of Mind, Columbia University, New York, New York, USA.

    • Gamaleldin F Elsayed
    •  & John P Cunningham
  4. Department of Statistics, Columbia University, New York, New York, USA.

    • John P Cunningham

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Contributions

G.F.E. and J.P.C. contributed to all aspects of this study.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to John P Cunningham.

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DOI

https://doi.org/10.1038/nn.4617

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