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Adaptive coding of visual information in neural populations

Author

Listed:
  • Diego A. Gutnisky

    (University of Texas-Houston Medical School, Houston, Texas 77030, USA)

  • Valentin Dragoi

    (University of Texas-Houston Medical School, Houston, Texas 77030, USA)

Abstract

Individual neurons at multiple stages of the visual system adapt to constant features in the visual scene, but how adaptation alters population dynamics is unknown. This paper shows that in the macaque primary visual cortex (V1), adaptation to briefly presented oriented stimuli alters pairwise correlations, with implications for the efficiency of the population code.

Suggested Citation

  • Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7184:d:10.1038_nature06563
    DOI: 10.1038/nature06563
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    Citations

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    Cited by:

    1. 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.
    2. Ashok Litwin-Kumar & Anne-Marie M Oswald & Nathaniel N Urban & Brent Doiron, 2011. "Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-14, December.
    3. Arno Onken & Valentin Dragoi & Klaus Obermayer, 2012. "A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-12, June.
    4. Wensheng Sun & Dennis L Barbour, 2017. "Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex," PLOS Biology, Public Library of Science, vol. 15(11), pages 1-22, November.
    5. 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.
    6. Christopher F. Angeloni & Wiktor Młynarski & Eugenio Piasini & Aaron M. Williams & Katherine C. Wood & Linda Garami & Ann M. Hermundstad & Maria N. Geffen, 2023. "Dynamics of cortical contrast adaptation predict perception of signals in noise," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    7. Jerome Carriot & Graham McAllister & Hamed Hooshangnejad & Isabelle Mackrous & Kathleen E. Cullen & Maurice J. Chacron, 2022. "Sensory adaptation mediates efficient and unambiguous encoding of natural stimuli by vestibular thalamocortical pathways," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Ru-Yuan Zhang & Xue-Xin Wei & Kendrick Kay, 2020. "Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-29, August.
    9. Vikranth R Bejjanki & Rava Azeredo da Silveira & Jonathan D Cohen & Nicholas B Turk-Browne, 2017. "Noise correlations in the human brain and their impact on pattern classification," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-23, August.
    10. Geoffrey Terral & Evan Harrell & Gabriel Lepousez & Yohan Wards & Dinghuang Huang & Tiphaine Dolique & Giulio Casali & Antoine Nissant & Pierre-Marie Lledo & Guillaume Ferreira & Giovanni Marsicano & , 2024. "Endogenous cannabinoids in the piriform cortex tune olfactory perception," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    11. Rava Azeredo da Silveira & Michael J Berry II, 2014. "High-Fidelity Coding with Correlated Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-25, November.
    12. Ingmar Kanitscheider & Ruben Coen-Cagli & Adam Kohn & Alexandre Pouget, 2015. "Measuring Fisher Information Accurately in Correlated Neural Populations," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-27, June.
    13. 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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