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Adaptive coding across visual features during free-viewing and fixation conditions

Author

Listed:
  • Sunny Nigam

    (University of Texas at Houston)

  • Russell Milton

    (University of Texas at Houston)

  • Sorin Pojoga

    (University of Texas at Houston)

  • Valentin Dragoi

    (University of Texas at Houston
    Rice University)

Abstract

Theoretical studies have long proposed that adaptation allows the brain to effectively use the limited response range of sensory neurons to encode widely varying natural inputs. However, despite this influential view, experimental studies have exclusively focused on how the neural code adapts to a range of stimuli lying along a single feature axis, such as orientation or contrast. Here, we performed electrical recordings in macaque visual cortex (area V4) to reveal significant adaptive changes in the neural code of single cells and populations across multiple feature axes. Both during free viewing and passive fixation, populations of cells improved their ability to encode image features after rapid exposure to stimuli lying on orthogonal feature axes even in the absence of initial tuning to these stimuli. These results reveal a remarkable adaptive capacity of visual cortical populations to improve network computations relevant for natural viewing despite the modularity of the functional cortical architecture.

Suggested Citation

  • Sunny Nigam & Russell Milton & Sorin Pojoga & Valentin Dragoi, 2023. "Adaptive coding across visual features during free-viewing and fixation conditions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35656-w
    DOI: 10.1038/s41467-022-35656-w
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    References listed on IDEAS

    as
    1. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    2. Valentin Dragoi & Casto Rivadulla & Mriganka Sur, 2001. "Foci of orientation plasticity in visual cortex," Nature, Nature, vol. 411(6833), pages 80-86, May.
    3. Masoud Ghodrati & Elizabeth Zavitz & Marcello G. P. Rosa & Nicholas S. C. Price, 2019. "Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    4. Berens, Philipp, 2009. "CircStat: A MATLAB Toolbox for Circular Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i10).
    5. Russell Milton & Neda Shahidi & Valentin Dragoi, 2020. "Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Elaine Tring & Mario Dipoppa & Dario L. Ringach, 2023. "A power law describes the magnitude of adaptation in neural populations of primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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