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A primacy code for odor identity

Author

Listed:
  • Christopher D. Wilson

    (NYU Neuroscience Institute, New York University Langone Medical Center)

  • Gabriela O. Serrano

    (NYU Neuroscience Institute, New York University Langone Medical Center)

  • Alexei A. Koulakov

    (Cold Spring Harbor Laboratory, Cold Spring Harbor)

  • Dmitry Rinberg

    (NYU Neuroscience Institute, New York University Langone Medical Center
    Center for Neural Science, New York University)

Abstract

Humans can identify visual objects independently of view angle and lighting, words independently of volume and pitch, and smells independently of concentration. The computational principles underlying invariant object recognition remain mostly unknown. Here we propose that, in olfaction, a small and relatively stable set comprised of the earliest activated receptors forms a code for concentration-invariant odor identity. One prediction of this “primacy coding” scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring

Suggested Citation

  • Christopher D. Wilson & Gabriela O. Serrano & Alexei A. Koulakov & Dmitry Rinberg, 2017. "A primacy code for odor identity," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01432-4
    DOI: 10.1038/s41467-017-01432-4
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    Cited by:

    1. David Zwicker, 2019. "Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-22, July.
    2. Rishabh Chandak & Baranidharan Raman, 2023. "Neural manifolds for odor-driven innate and acquired appetitive preferences," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    3. Yao, Chenggui & Sun, JianQiang & Jin, Jun & Shuai, Jianwei & Li, Xiang & Yao, Yuangen & Xu, Xufan, 2023. "The power law statistics of the spiking timing in a neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

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