IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v571y2019i7765d10.1038_s41586-019-1346-5.html
   My bibliography  Save this article

High-dimensional geometry of population responses in visual cortex

Author

Listed:
  • Carsen Stringer

    (HHMI Janelia Research Campus
    University College London)

  • Marius Pachitariu

    (HHMI Janelia Research Campus
    University College London)

  • Nicholas Steinmetz

    (University College London
    University of Washington)

  • Matteo Carandini

    (University College London)

  • Kenneth D. Harris

    (University College London)

Abstract

A neuronal population encodes information most efficiently when its stimulus responses are high-dimensional and uncorrelated, and most robustly when they are lower-dimensional and correlated. Here we analysed the dimensionality of the encoding of natural images by large populations of neurons in the visual cortex of awake mice. The evoked population activity was high-dimensional, and correlations obeyed an unexpected power law: the nth principal component variance scaled as 1/n. This scaling was not inherited from the power law spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that if the variance spectrum was to decay more slowly then the population code could not be smooth, allowing small changes in input to dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness may represent a fundamental constraint that determines correlations in neural population codes.

Suggested Citation

  • Carsen Stringer & Marius Pachitariu & Nicholas Steinmetz & Matteo Carandini & Kenneth D. Harris, 2019. "High-dimensional geometry of population responses in visual cortex," Nature, Nature, vol. 571(7765), pages 361-365, July.
  • Handle: RePEc:nat:nature:v:571:y:2019:i:7765:d:10.1038_s41586-019-1346-5
    DOI: 10.1038/s41586-019-1346-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-019-1346-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-019-1346-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. W. Jeffrey Johnston & Stefano Fusi, 2023. "Abstract representations emerge naturally in neural networks trained to perform multiple tasks," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Mehrabbeik, Mahtab & Shams-Ahmar, Mohammad & Levine, Alexandra T. & Jafari, Sajad & Merrikhi, Yaser, 2022. "Distinctive nonlinear dimensionality of neural spiking activity in extrastriate cortex during spatial working memory; a Higuchi fractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    3. Kristjan Kalm & Dennis Norris, 2021. "Sequence learning recodes cortical representations instead of strengthening initial ones," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-34, May.
    4. Yuke Yan & James M. Goodman & Dalton D. Moore & Sara A. Solla & Sliman J. Bensmaia, 2020. "Unexpected complexity of everyday manual behaviors," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    5. Jérémie Sibille & Carolin Gehr & Jonathan I. Benichov & Hymavathy Balasubramanian & Kai Lun Teh & Tatiana Lupashina & Daniela Vallentin & Jens Kremkow, 2022. "High-density electrode recordings reveal strong and specific connections between retinal ganglion cells and midbrain neurons," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    6. Ege Altan & Sara A Solla & Lee E Miller & Eric J Perreault, 2021. "Estimating the dimensionality of the manifold underlying multi-electrode neural recordings," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-23, November.
    7. Edward A. B. Horrocks & Fabio R. Rodrigues & Aman B. Saleem, 2024. "Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    8. Ghislain St-Yves & Emily J. Allen & Yihan Wu & Kendrick Kay & Thomas Naselaris, 2023. "Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    9. Rong J. B. Zhu & Xue-Xin Wei, 2023. "Unsupervised approach to decomposing neural tuning variability," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    10. Patrice Abry & B. Cooper Boniece & Gustavo Didier & Herwig Wendt, 2023. "Wavelet eigenvalue regression in high dimensions," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 1-32, April.
    11. Maximilian Hoffmann & Jörg Henninger & Johannes Veith & Lars Richter & Benjamin Judkewitz, 2023. "Blazed oblique plane microscopy reveals scale-invariant inference of brain-wide population activity," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    12. Disheng Tang & Joel Zylberberg & Xiaoxuan Jia & Hannah Choi, 2024. "Stimulus type shapes the topology of cellular functional networks in mouse visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    13. Kenneth W. Latimer & David J. Freedman, 2023. "Low-dimensional encoding of decisions in parietal cortex reflects long-term training history," Nature Communications, Nature, vol. 14(1), pages 1-24, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:571:y:2019:i:7765:d:10.1038_s41586-019-1346-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.