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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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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.

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