IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000053.html
   My bibliography  Save this article

Efficient Olfactory Coding in the Pheromone Receptor Neuron of a Moth

Author

Listed:
  • Lubomir Kostal
  • Petr Lansky
  • Jean-Pierre Rospars

Abstract

The concept of coding efficiency holds that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical characteristics of their natural stimulus. Encouraged by the successful invocation of this principle to predict how neurons encode natural auditory and visual stimuli, we attempted its application to olfactory neurons. The pheromone receptor neuron of the male moth Antheraea polyphemus, for which quantitative properties of both the natural stimulus and the reception processes are available, was selected. We predicted several characteristics that the pheromone plume should possess under the hypothesis that the receptors perform optimally, i.e., transfer as much information on the stimulus per unit time as possible. Our results demonstrate that the statistical characteristics of the predicted stimulus, e.g., the probability distribution function of the stimulus concentration, the spectral density function of the stimulation course, and the intermittency, are in good agreement with those measured experimentally in the field. These results should stimulate further quantitative studies on the evolutionary adaptation of olfactory nervous systems to odorant plumes and on the plume characteristics that are most informative for the ‘sniffer’. Both aspects are relevant to the design of olfactory sensors for odour-tracking robots.Author Summary: Efficient coding is an overarching principle, well tested in visual and auditory neurobiology, which states that sensory neurons are adapted to the statistical characteristics of their natural stimulus - in brief, neurons best process those stimuli that occur most frequently. To assess its validity in olfaction, we examine the pheromone communication of moths, in which males locate their female mates by the pheromone they release. We determine the characteristics of the pheromone plume which are best detected by the male reception system. We show that they are in agreement with plume measurements in the field, so providing quantitative evidence that this system also obeys the efficient coding principle. Exploring the quantitative relationship between the properties of biological sensory systems and their natural environment should lead not only to a better understanding of neural functions and evolutionary processes, but also to improvements in the design of artificial sensory systems.

Suggested Citation

  • Lubomir Kostal & Petr Lansky & Jean-Pierre Rospars, 2008. "Efficient Olfactory Coding in the Pheromone Receptor Neuron of a Moth," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-11, April.
  • Handle: RePEc:plo:pcbi00:1000053
    DOI: 10.1371/journal.pcbi.1000053
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000053
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000053&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1000053?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. Evan C. Smith & Michael S. Lewicki, 2006. "Efficient auditory coding," Nature, Nature, vol. 439(7079), pages 978-982, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jean-Pierre Rospars & Alexandre Grémiaux & David Jarriault & Antoine Chaffiol & Christelle Monsempes & Nina Deisig & Sylvia Anton & Philippe Lucas & Dominique Martinez, 2014. "Heterogeneity and Convergence of Olfactory First-Order Neurons Account for the High Speed and Sensitivity of Second-Order Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-16, December.
    2. Marie Levakova & Lubomir Kostal & Christelle Monsempès & Vincent Jacob & Philippe Lucas, 2018. "Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-17, November.

    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. Noga Mosheiff & Haggai Agmon & Avraham Moriel & Yoram Burak, 2017. "An efficient coding theory for a dynamic trajectory predicts non-uniform allocation of entorhinal grid cells to modules," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-19, June.
    2. Sam V Norman-Haignere & Josh H McDermott, 2018. "Neural responses to natural and model-matched stimuli reveal distinct computations in primary and nonprimary auditory cortex," PLOS Biology, Public Library of Science, vol. 16(12), pages 1-46, December.
    3. Jonathan J Hunt & Peter Dayan & Geoffrey J Goodhill, 2013. "Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-17, May.
    4. Jacob N Oppenheim & Pavel Isakov & Marcelo O Magnasco, 2013. "Degraded Time-Frequency Acuity to Time-Reversed Notes," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
    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. Gonzalo H Otazu & Christian Leibold, 2011. "A Corticothalamic Circuit Model for Sound Identification in Complex Scenes," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-15, September.
    7. Lingyun Zhao & Li Zhaoping, 2011. "Understanding Auditory Spectro-Temporal Receptive Fields and Their Changes with Input Statistics by Efficient Coding Principles," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-16, August.
    8. Tomas Barta & Lubomir Kostal, 2019. "The effect of inhibition on rate code efficiency indicators," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-21, December.
    9. Philippe Albouy & Samuel A. Mehr & Roxane S. Hoyer & Jérémie Ginzburg & Yi Du & Robert J. Zatorre, 2024. "Spectro-temporal acoustical markers differentiate speech from song across cultures," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    10. Oded Barzelay & Miriam Furst & Omri Barak, 2017. "A New Approach to Model Pitch Perception Using Sparse Coding," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-36, January.
    11. Joseph D. Zak & Gautam Reddy & Vaibhav Konanur & Venkatesh N. Murthy, 2024. "Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    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:plo:pcbi00:1000053. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.