1. Neuroscience
Download icon

Odor identity coding by distributed ensembles of neurons in the mouse olfactory cortex

  1. Benjamin Roland
  2. Thomas Deneux
  3. Kevin M Franks
  4. Brice Bathellier  Is a corresponding author
  5. Alexander Fleischmann  Is a corresponding author
  1. Collège de France, France
  2. Centre National de la Recherche Scientifique, UPR 3293, France
  3. Duke University, United States
  4. Centre National de la Recherche Scientifique, France
Research Article
  • Cited 48
  • Views 4,107
  • Annotations
Cite this article as: eLife 2017;6:e26337 doi: 10.7554/eLife.26337

Abstract

Olfactory perception and behaviors critically depend on the ability to identify an odor across a wide range of concentrations. Here, we use calcium imaging to determine how odor identity is encoded in olfactory cortex. We find that, despite considerable trial-to-trial variability, odor identity can accurately be decoded from ensembles of co-active neurons that are distributed across piriform cortex without any apparent spatial organization. However, piriform response patterns change substantially over a 100-fold change in odor concentration, apparently degrading the population representation of odor identity. We show that this problem can be resolved by decoding odor identity from a subpopulation of concentration-invariant piriform neurons. These concentration-invariant neurons are overrepresented in piriform cortex but not in olfactory bulb mitral and tufted cells. We therefore propose that distinct perceptual features of odors are encoded in independent subnetworks of neurons in the olfactory cortex.

Article and author information

Author details

  1. Benjamin Roland

    Center for Interdisciplinary Research in Biology, Collège de France, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  2. Thomas Deneux

    Unité de Neuroscience, Information et Complexité, Centre National de la Recherche Scientifique, UPR 3293, Gif-sur-Yvette, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Kevin M Franks

    Department of Neurobiology, Duke University, Durham, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6386-9518
  4. Brice Bathellier

    Unité de Neuroscience, Information et Complexité, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
    For correspondence
    bathellier@unic.cnrs-gif.fr
    Competing interests
    The authors declare that no competing interests exist.
  5. Alexander Fleischmann

    Center for Interdisciplinary Research in Biology, Collège de France, Paris, France
    For correspondence
    alexander.fleischmann@college-de-france.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7956-9096

Funding

Marie Curie International Reintegration Grant (IRG 276869)

  • Alexander Fleischmann

Fondation pour la Recherche Médicale (AJE201106)

  • Alexander Fleischmann

European Molecular Biology Organization (ASTF 395 - 2014)

  • Benjamin Roland

LabEx Memolife

  • Benjamin Roland

National Institute on Deafness and Other Communication Disorders (DC009839 and DC015525)

  • Kevin M Franks

Agence Nationale de la Recherche (SENSEMAKER)

  • Brice Bathellier

Human Frontier Science Program (CDA-0064-2015)

  • Brice Bathellier

Marie Curie Program (CIG 334581)

  • Brice Bathellier

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

Ethics

Animal experimentation: This study was performed in strict accordance with French National and INSERM animal care and use committee guidelines (#B750512/00615.02). All surgery was performed under ketamine/xylazine anesthesia.

Reviewing Editor

  1. Upinder Singh Bhalla, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Publication history

  1. Received: February 24, 2017
  2. Accepted: April 29, 2017
  3. Accepted Manuscript published: May 10, 2017 (version 1)
  4. Version of Record published: May 19, 2017 (version 2)

Copyright

© 2017, Roland 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

  • 4,107
    Page views
  • 941
    Downloads
  • 48
    Citations

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

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)

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

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

  1. Further reading

Further reading

    1. Neuroscience
    James P Bohnslav et al.
    Tools and Resources Updated

    Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few behaviors, and variable across researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors of interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording hardware. It uses convolutional neural networks to compute motion, extract features from motion and images, and classify features into behaviors. Behaviors are classified with above 90% accuracy on single frames in videos of mice and flies, matching expert-level human performance. DeepEthogram accurately predicts rare behaviors, requires little training data, and generalizes across subjects. A graphical interface allows beginning-to-end analysis without end-user programming. DeepEthogram’s rapid, automatic, and reproducible labeling of researcher-defined behaviors of interest may accelerate and enhance supervised behavior analysis. Code is available at: https://github.com/jbohnslav/deepethogram.

    1. Developmental Biology
    2. Neuroscience
    Tania Moreno-Mármol et al.
    Research Article

    The vertebrate eye-primordium consists of a pseudostratified neuroepithelium, the optic vesicle (OV), in which cells acquire neural retina or retinal pigment epithelium (RPE) fates. As these fates arise, the OV assumes a cup-shape, influenced by mechanical forces generated within the neural retina. Whether the RPE passively adapts to retinal changes or actively contributes to OV morphogenesis remains unexplored. We generated a zebrafish Tg(E1-bhlhe40:GFP) line to track RPE morphogenesis and interrogate its participation in OV folding. We show that, in virtual absence of proliferation, RPE cells stretch and flatten, thereby matching the retinal curvature and promoting OV folding. Localized interference with the RPE cytoskeleton disrupts tissue stretching and OV folding. Thus, extreme RPE flattening and accelerated differentiation are efficient solutions adopted by fast-developing species to enable timely optic cup formation. This mechanism differs in amniotes, in which proliferation drives RPE expansion with a much-reduced need of cell flattening.