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Adaptive filtering enhances information transmission in visual cortex

Author

Listed:
  • Tatyana O. Sharpee

    (University of California, San Francisco
    University of California, San Francisco)

  • Hiroki Sugihara

    (University of California, San Francisco)

  • Andrei V. Kurgansky

    (University of California, San Francisco)

  • Sergei P. Rebrik

    (University of California, San Francisco)

  • Michael P. Stryker

    (University of California, San Francisco
    University of California, San Francisco)

  • Kenneth D. Miller

    (University of California, San Francisco
    University of California, San Francisco
    Columbia University Medical School, N.Y.S.P.I. Kolb Research Annex)

Abstract

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.

Suggested Citation

  • Tatyana O. Sharpee & Hiroki Sugihara & Andrei V. Kurgansky & Sergei P. Rebrik & Michael P. Stryker & Kenneth D. Miller, 2006. "Adaptive filtering enhances information transmission in visual cortex," Nature, Nature, vol. 439(7079), pages 936-942, February.
  • Handle: RePEc:nat:nature:v:439:y:2006:i:7079:d:10.1038_nature04519
    DOI: 10.1038/nature04519
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    Citations

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    Cited by:

    1. Miguel Maravall & Rasmus S Petersen & Adrienne L Fairhall & Ehsan Arabzadeh & Mathew E Diamond, 2007. "Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex," PLOS Biology, Public Library of Science, vol. 5(2), pages 1-12, January.
    2. Arne F Meyer & Jan-Philipp Diepenbrock & Max F K Happel & Frank W Ohl & Jörn Anemüller, 2014. "Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-15, April.
    3. Marcus H C Howlett & Robert G Smith & Maarten Kamermans, 2017. "A novel mechanism of cone photoreceptor adaptation," PLOS Biology, Public Library of Science, vol. 15(4), pages 1-28, April.
    4. Kaiser Niknam & Amir Akbarian & Kelsey Clark & Yasin Zamani & Behrad Noudoost & Neda Nategh, 2019. "Characterizing and dissociating multiple time-varying modulatory computations influencing neuronal activity," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-38, September.
    5. Jonathan Schaffner & Sherry Dongqi Bao & Philippe N. Tobler & Todd A. Hare & Rafael Polania, 2023. "Sensory perception relies on fitness-maximizing codes," Nature Human Behaviour, Nature, vol. 7(7), pages 1135-1151, July.
    6. Johnatan Aljadeff & Ronen Segev & Michael J Berry II & Tatyana O Sharpee, 2013. "Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-12, September.
    7. Klaus Wimmer & K Jannis Hildebrandt & R Matthias Hennig & Klaus Obermayer, 2008. "Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2," PLOS Computational Biology, Public Library of Science, vol. 4(9), pages 1-18, September.

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