Evidence map›Paper›PMID 29687778›Full record

ArticleeLife2018

Antagonism in olfactory receptor neurons and its implications for the perception of odor mixtures.

Gautam Reddy, Joseph D Zak, Massimo Vergassola, Venkatesh N Murthy

Abstract read
In one paragraph

Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed.

  1. Article
  2. Neurophysiological Responses to Inhalation ofPlants (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Article
  7. When noncanonical olfaction is optimal.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Design Principles From Natural Olfaction for Electronic Noses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gautam Reddy *Department of Physics, University of California, San Diego, La Jolla, United States.ORCID 0000-0002-1276-9613
Joseph D Zak *Department of Molecular Cellular Biology, Harvard University, Cambridge, United States.ORCID 0000-0002-3148-5325
Massimo VergassolaDepartment of Physics, University of California, San Diego, La Jolla, United States.
Venkatesh N MurthyDepartment of Molecular Cellular Biology, Harvard University, Cambridge, United States.ORCID 0000-0003-2443-4252

Funding

Cortical feedback and olfactory processingR01DC016289 · NIDCD · HARVARD UNIVERSITY · PI MURTHY, VENKATESH N · 2018 to 2022
$2.0M
Neuromodulation of sensory processing by the serotonin systemR01DC014453 · NIDCD · HARVARD UNIVERSITY · PI MURTHY, VENKATESH N · 2015 to 2019
$1.8M
Information Coding in Individual Olfactory Sensory AxonsF32DC015938 · NIDCD · HARVARD UNIVERSITY · PI ZAK, JOSEPH DONALD · 2017 to 2019
$165k
NIDCD NIH HHS F32 DC015938NIDCD NIH HHS R01 DC014453NIDCD NIH HHS R01 DC016289NIH HHS F32 DC015938NIH HHS R01 DC014453
6 · The paper itself

Abstract

Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Yet, the response of ORNs to mixtures is strongly non-additive, and exhibits antagonistic interactions among odorants. Here, we model the processing of mixtures by mammalian ORNs, focusing on the role of inhibitory mechanisms. We show how antagonism leads to an effective 'normalization' of the ensemble ORN response, that is, the distribution of responses of the ORN population induced by any mixture is largely independent of the number of components in the mixture. This property arises from a novel mechanism involving the distinct statistical properties of receptor binding and activation, without any recurrent neuronal circuitry. Normalization allows our encoding model to outperform non-interacting models in odor discrimination tasks, leads to experimentally testable predictions and explains several psychophysical experiments in humans.

Indexed as

OdorantsOlfactory PerceptionAction PotentialsHumansModels, NeurologicalOlfactory Receptor NeuronsSmellcomputational biologydecodingencodingmixture interactionneurosciencenonenormalizationodor perceptionsystems biology

Identifiers

PMID29687778
PMCPMC5915184

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.