Evidence map›Paper›PMID 42627971›Full record

ArticleCell reports2026

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

Hannah McCalmon, George Cai, Constantine Tsibouris, Noé Hamou, Jo Hoskins, Farhad Pashakhanloo, SueYeon Chung, Vikrant Kapoor, Venkatesh N Murthy

Abstract read
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In one paragraph

Article in Cell reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Hannah McCalmonDepartment of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
George CaiKempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02138, USA; Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA.
Constantine TsibourisDepartment of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
Noé HamouDepartment of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
Jo HoskinsDepartment of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
Farhad PashakhanlooCenter for Brain Science, Harvard University, Cambridge, MA 02138, USA.
SueYeon ChungKempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA; Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA; Center for Neural Science, New York University, New York, NY 10012, USA; Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA.
Vikrant KapoorDepartment of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. Electronic address: vkapoor@fas.harvard.edu.
Venkatesh N MurthyDepartment of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. Electronic address: vnmurthy@fas.harvard.edu.

Funding

Stability of Odor Representations in the Olfactory TubercleF31DC023435 · NIDCD · HARVARD UNIVERSITY · PI MCCALMON, HANNAH · 2025 to 2025
$46k
NIDCD NIH HHS F31 DC023435
6 · The paper itself

Abstract

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.

Indexed as

Discrimination, PsychologicalOdorantsOlfactory BulbOlfactory PerceptionSmellAnimalsMaleMiceMice, Inbred C57BLbehaviorbiophysical modelCP: neurosciencemanifold geometryolfactionolfactory bulbsensory discrimination

Identifiers

PMID42627971

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.