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Peer-reviewedCC BY-NCCell reports

Sparse input representations explain odor discrimination in complex, concentration-varying mixtures

  • Hannah McCalmon1
  • George Cai2
  • Constantine Tsibouris1
  • Noé Hamou1
  • Jo Hoskins1
  • Farhad Pashakhanloo3
  • SueYeon Chung2
  • Vikrant Kapoor1
  • Venkatesh N. Murthy1
  1. 1Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
  2. 2Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02138, USA
  3. 3Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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Abstract

SUMMARY: In natural environments, animals must detect behaviorally relevant odors despite variability in both odor mixture composition and stimulus intensity. Although mice can identify salient odors embedded in complex mixtures, how target concentration and background complexity jointly constrain discrimination remains unclear. We trained mice in a two-alternative forced choice task to identify target odors embedded in mixtures containing up to 16 background components. After performance stabilized, we systematically varied target odor concentration. Discrimination accuracy declined with decreasing target concentration but showed little additional dependence on background complexity. Using a biophysically grounded model of olfactory bulb glomerular responses, we show that linear decoding reproduces behavioral performance when intrinsic neural noise dominates over background-driven variability. Manifold capacity analysis revealed that neural representations remain efficiently structured for odor discrimination despite variation in background complexity. These results define a noise-limited regime of olfactory discrimination in which target detectability is primarily constrained by neural sensitivity rather than background interference.

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Cite

BibTeXRIS
BibTeX
@article{mccalmon2026sparse,
  title = {{Sparse input representations explain odor discrimination in complex, concentration-varying mixtures}},
  author = {Hannah McCalmon and George Cai and Constantine Tsibouris and Noé Hamou and Jo Hoskins and Farhad Pashakhanloo and SueYeon Chung and Vikrant Kapoor and Venkatesh N. Murthy},
  journal = {Cell reports},
  year = {2026},
  month = aug,
  doi = {10.1016/j.celrep.2026.117883},
  url = {https://europepmc.org/article/PMC/PMC13730461},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Sparse input representations explain odor discrimination in complex, concentration-varying mixtures
AU  - Hannah McCalmon
AU  - George Cai
AU  - Constantine Tsibouris
AU  - Noé Hamou
AU  - Jo Hoskins
AU  - Farhad Pashakhanloo
AU  - SueYeon Chung
AU  - Vikrant Kapoor
AU  - Venkatesh N. Murthy
PY  - 2026
DA  - 2026/08/20
JO  - Cell reports
DO  - 10.1016/j.celrep.2026.117883
UR  - https://europepmc.org/article/PMC/PMC13730461
AB  - SUMMARY: In natural environments, animals must detect behaviorally relevant odors despite variability in both odor mixture composition and stimulus intensity. Although mice can identify salient odors embedded in complex mixtures, how target concentration and background complexity jointly constrain discrimination remains unclear. We trained mice in a two-alternative forced choice task to identify target odors embedded in mixtures containing up to 16 background components. After performance stabilized, we systematically varied target odor concentration. Discrimination accuracy declined with decreasing target concentration but showed little additional dependence on background complexity. Using a biophysically grounded model of olfactory bulb glomerular responses, we show that linear decoding reproduces behavioral performance when intrinsic neural noise dominates over background-driven variability. Manifold capacity analysis revealed that neural representations remain efficiently structured for odor discrimination despite variation in background complexity. These results define a noise-limited regime of olfactory discrimination in which target detectability is primarily constrained by neural sensitivity rather than background interference.
SN  - 2211-1247
ER  -