Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Concentration invariant odor coding

Christopher D. Wilson, Gabriela O. Serrano, Alexei A. Koulakov, Dmitry Rinberg
doi: https://doi.org/10.1101/125039
Christopher D. Wilson
1NYU Neuroscience Institute, New York University Langone Medical Center, New York, NY 10016
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gabriela O. Serrano
1NYU Neuroscience Institute, New York University Langone Medical Center, New York, NY 10016
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alexei A. Koulakov
2Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dmitry Rinberg
1NYU Neuroscience Institute, New York University Langone Medical Center, New York, NY 10016
3Center for Neural Science, New York University, New York, NY 10003
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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 made 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 <100 ms after inhalation onset to identify odors. Using multi-electrode array recordings of odor responses in the olfactory bulb, we find that concentration invariant units respond earliest and at latencies that are within this behaviorally-defined time window. We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 06, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Concentration invariant odor coding
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Concentration invariant odor coding
Christopher D. Wilson, Gabriela O. Serrano, Alexei A. Koulakov, Dmitry Rinberg
bioRxiv 125039; doi: https://doi.org/10.1101/125039
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Concentration invariant odor coding
Christopher D. Wilson, Gabriela O. Serrano, Alexei A. Koulakov, Dmitry Rinberg
bioRxiv 125039; doi: https://doi.org/10.1101/125039

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4235)
  • Biochemistry (9136)
  • Bioengineering (6784)
  • Bioinformatics (24001)
  • Biophysics (12129)
  • Cancer Biology (9534)
  • Cell Biology (13778)
  • Clinical Trials (138)
  • Developmental Biology (7636)
  • Ecology (11702)
  • Epidemiology (2066)
  • Evolutionary Biology (15513)
  • Genetics (10644)
  • Genomics (14326)
  • Immunology (9483)
  • Microbiology (22839)
  • Molecular Biology (9090)
  • Neuroscience (48995)
  • Paleontology (355)
  • Pathology (1482)
  • Pharmacology and Toxicology (2570)
  • Physiology (3846)
  • Plant Biology (8331)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6192)
  • Zoology (1301)