IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-30348-x.html
   My bibliography  Save this article

Sensory adaptation mediates efficient and unambiguous encoding of natural stimuli by vestibular thalamocortical pathways

Author

Listed:
  • Jerome Carriot

    (McGill University)

  • Graham McAllister

    (McGill University)

  • Hamed Hooshangnejad

    (Johns Hopkins University)

  • Isabelle Mackrous

    (McGill University)

  • Kathleen E. Cullen

    (Johns Hopkins University
    Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Maurice J. Chacron

    (McGill University)

Abstract

Sensory systems must continuously adapt to optimally encode stimuli encountered within the natural environment. The prevailing view is that such optimal coding comes at the cost of increased ambiguity, yet to date, prior studies have focused on artificial stimuli. Accordingly, here we investigated whether such a trade-off between optimality and ambiguity exists in the encoding of natural stimuli in the vestibular system. We recorded vestibular nuclei and their target vestibular thalamocortical neurons during naturalistic and artificial self-motion stimulation. Surprisingly, we found no trade-off between optimality and ambiguity. Using computational methods, we demonstrate that thalamocortical neural adaptation in the form of contrast gain control actually reduces coding ambiguity without compromising the optimality of coding under naturalistic but not artificial stimulation. Thus, taken together, our results challenge the common wisdom that adaptation leads to ambiguity and instead suggest an essential role in underlying unambiguous optimized encoding of natural stimuli.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30348-x
    DOI: 10.1038/s41467-022-30348-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-30348-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-30348-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    3. Michael Lohse & Victoria M. Bajo & Andrew J. King & Ben D. B. Willmore, 2020. "Neural circuits underlying auditory contrast gain control and their perceptual implications," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher F. Angeloni & Wiktor MÅ‚ynarski & 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.
    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. 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.
    4. Gabriel D Puccini & Albert Compte & Miguel Maravall, 2006. "Stimulus Dependence of Barrel Cortex Directional Selectivity," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-6, December.
    5. 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.
    6. Omer Mano & Damon A Clark, 2017. "Graphics Processing Unit-Accelerated Code for Computing Second-Order Wiener Kernels and Spike-Triggered Covariance," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-11, January.
    7. 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.
    8. Matthew F. Tang & Ehsan Kheradpezhouh & Conrad C. Y. Lee & J. Edwin Dickinson & Jason B. Mattingley & Ehsan Arabzadeh, 2023. "Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Jeffrey D Fitzgerald & Ryan J Rowekamp & Lawrence C Sincich & Tatyana O Sharpee, 2011. "Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-9, October.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.
    18. Sungho Hong & Brian Nils Lundstrom & Adrienne L Fairhall, 2008. "Intrinsic Gain Modulation and Adaptive Neural Coding," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-11, July.
    19. 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.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30348-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.