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Attentional fluctuations induce shared variability in macaque primary visual cortex

Author

Listed:
  • George H. Denfield

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Alexander S. Ecker

    (Baylor College of Medicine
    Baylor College of Medicine
    University of Tübingen
    Bernstein Centre for Computational Neuroscience)

  • Tori J. Shinn

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Matthias Bethge

    (Baylor College of Medicine
    University of Tübingen
    Bernstein Centre for Computational Neuroscience
    Max Planck Institute for Biological Cybernetics)

  • Andreas S. Tolias

    (Baylor College of Medicine
    Baylor College of Medicine
    Bernstein Centre for Computational Neuroscience
    Rice University)

Abstract

Variability in neuronal responses to identical stimuli is frequently correlated across a population. Attention is thought to reduce these correlations by suppressing noisy inputs shared by the population. However, even with precise control of the visual stimulus, the subject’s attentional state varies across trials. While these state fluctuations are bound to induce some degree of correlated variability, it is currently unknown how strong their effect is, as previous studies generally do not dissociate changes in attentional strength from changes in attentional state variability. We designed a novel paradigm that does so and find both a pronounced effect of attentional fluctuations on correlated variability at long timescales and attention-dependent reductions in correlations at short timescales. These effects predominate in layers 2/3, as expected from a feedback signal such as attention. Thus, significant portions of correlated variability can be attributed to fluctuations in internally generated signals, like attention, rather than noise.

Suggested Citation

  • George H. Denfield & Alexander S. Ecker & Tori J. Shinn & Matthias Bethge & Andreas S. Tolias, 2018. "Attentional fluctuations induce shared variability in macaque primary visual cortex," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05123-6
    DOI: 10.1038/s41467-018-05123-6
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    Cited by:

    1. Max F Burg & Santiago A Cadena & George H Denfield & Edgar Y Walker & Andreas S Tolias & Matthias Bethge & Alexander S Ecker, 2021. "Learning divisive normalization in primary visual cortex," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-31, June.
    2. Qianli Yang & Edgar Walker & R. James Cotton & Andreas S. Tolias & Xaq Pitkow, 2021. "Revealing nonlinear neural decoding by analyzing choices," Nature Communications, Nature, vol. 12(1), pages 1-13, December.

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