Most primary olfactory neurons have individually neutral effects on behavior

  1. Tayfun Tumkaya
  2. Safwan Burhanudin
  3. Asghar Khalilnezhad
  4. James Stewart
  5. Hyungwon Choi
  6. Adam Claridge-Chang  Is a corresponding author
  1. A*STAR, Singapore
  2. Duke-NUS Medical School, Singapore
  3. National University of Singapore, Singapore

Abstract

Animals use olfactory receptors to navigate mates, food, and danger. However, for complex olfactory systems, it is unknown what proportion of primary olfactory sensory neurons can individually drive avoidance or attraction. Similarly, the rules that govern behavioral responses to receptor combinations are unclear. We used optogenetic analysis in Drosophila to map the behavior elicited by olfactory-receptor neuron (ORN) classes: just one-fifth of ORN-types drove either avoidance or attraction. Although wind and hunger are closely linked to olfaction, neither had much effect on single-class responses. Several pooling rules have been invoked to explain how ORN types combine their behavioral influences; we activated two-way combinations and compared patterns of single- and double-ORN responses: these comparisons were inconsistent with simple pooling. We infer that the majority of primary olfactory sensory neurons have neutral behavioral effects individually, but participate in broad, odor-elicited ensembles with potent behavioral effects arising from complex interactions.

Data availability

Data and code availability:All of the data generated by this study are available to download from Zenodo (https://doi.org/10.5281/zenodo.3994033). The code is available at https://github.com/ttumkaya/WALiSuite_V2.0.

The following data sets were generated

Article and author information

Author details

  1. Tayfun Tumkaya

    Institute for Molecular and Cell Biology, A*STAR, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8425-3360
  2. Safwan Burhanudin

    Institute for Molecular and Cell Biology, A*STAR, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  3. Asghar Khalilnezhad

    Institute for Molecular and Cell Biology, A*STAR, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  4. James Stewart

    Program in Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  5. Hyungwon Choi

    Department of Medicine, National University of Singapore, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6687-3088
  6. Adam Claridge-Chang

    Program in Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore, Singapore
    For correspondence
    claridge-chang.adam@duke-nus.edu.sg
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4583-3650

Funding

Agency for Science, Technology and Research (AGA-SINGA)

  • Tayfun Tumkaya

Agency for Science, Technology and Research (Block grant)

  • Tayfun Tumkaya
  • James Stewart
  • Hyungwon Choi
  • Adam Claridge-Chang

Ministry of Education - Singapore (MOE2013-T2-2-054)

  • Tayfun Tumkaya
  • James Stewart
  • Adam Claridge-Chang

Ministry of Education - Singapore (MOE2017-T2-1-089)

  • Tayfun Tumkaya
  • James Stewart
  • Adam Claridge-Chang

Ministry of Education - Singapore (MOE-2016-T2-1-001)

  • Hyungwon Choi

National Medical Research Council (NMRC-CG-2017-M009)

  • Hyungwon Choi

Duke-NUS Medical School (Block grant)

  • Adam Claridge-Chang

Agency for Science, Technology and Research (JCO-1231AFG030)

  • James Stewart
  • Adam Claridge-Chang

Agency for Science, Technology and Research (JCO-1431AFG120)

  • James Stewart
  • Adam Claridge-Chang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Sonia Sen, Tata Institute for Genetics and Society, India

Publication history

  1. Preprint posted: June 10, 2021 (view preprint)
  2. Received: June 13, 2021
  3. Accepted: January 17, 2022
  4. Accepted Manuscript published: January 19, 2022 (version 1)
  5. Accepted Manuscript updated: January 21, 2022 (version 2)
  6. Version of Record published: February 1, 2022 (version 3)
  7. Version of Record updated: May 16, 2022 (version 4)

Copyright

© 2022, Tumkaya et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,666
    Page views
  • 242
    Downloads
  • 4
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Tayfun Tumkaya
  2. Safwan Burhanudin
  3. Asghar Khalilnezhad
  4. James Stewart
  5. Hyungwon Choi
  6. Adam Claridge-Chang
(2022)
Most primary olfactory neurons have individually neutral effects on behavior
eLife 11:e71238.
https://doi.org/10.7554/eLife.71238

Further reading

    1. Neuroscience
    Davide Reato, Raphael Steinfeld ... Alfonso Renart
    Research Article Updated

    Sensory responses of cortical neurons are more discriminable when evoked on a baseline of desynchronized spontaneous activity, but cortical desynchronization has not generally been associated with more accurate perceptual decisions. Here, we show that mice perform more accurate auditory judgments when activity in the auditory cortex is elevated and desynchronized before stimulus onset, but only if the previous trial was an error, and that this relationship is occluded if previous outcome is ignored. We confirmed that the outcome-dependent effect of brain state on performance is neither due to idiosyncratic associations between the slow components of either signal, nor to the existence of specific cortical states evident only after errors. Instead, errors appear to gate the effect of cortical state fluctuations on discrimination accuracy. Neither facial movements nor pupil size during the baseline were associated with accuracy, but they were predictive of measures of responsivity, such as the probability of not responding to the stimulus or of responding prematurely. These results suggest that the functional role of cortical state on behavior is dynamic and constantly regulated by performance monitoring systems.

    1. Neuroscience
    Lucas Benjamin, Ana Fló ... Ghislaine Dehaene-Lambertz
    Research Article Updated

    Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here, we used the formalism proposed by network science to study the representation of local and higher-order structures and their interaction in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order network structures such as communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but on a parsimonious representation of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly accounts for previous findings, including local transition probabilities as well as high-order network structures, unifying sequence learning across scales. We finally propose putative brain implementations of such bias.