{
  "abstract": "Neural representations in the olfactory bulb (OB) remain poorly understood compared to well-established coding principles in other sensory systems. This difficulty arises because a natural coordinate system for olfactory stimulus space has not yet been identified, complicating the characterization of neural receptive fields analogous to those in other sensory modalities. Moreover, the extent to which these tuning properties are spatially organized within the OB remains unclear. To advance our understanding of the olfactory coding, we introduce an interpretable method of Shapley fields, as an olfactory analog of retinotopic receptive fields. Shapley fields are spatial distributions of chemical feature importance for the tuning of OB glomeruli. We used this tool to investigate chemotopy in the OB with diverse sets of chemical features using widefield epifluorescence recordings of the mouse dorsal OB in response to stimuli across a wide range of the chemical space. We found that Shapley fields reveal a weak chemotopic organization of the chemical feature sensitivity of dorsal OB glomeruli. This organization was consistent across animals and mostly agreed across very different chemical feature sets: (i) the expert-curated PubChem database features and (ii) features derived from a Graph Neural Network trained on human olfactory perceptual tasks. This chemotopy involved at least three chemical feature dimensions, rather than one or two dominant properties. Moreover, we found that the principal components of the Shapley fields corresponded to single commonly accepted chemical classification groups. This implies that these chemical groups could have been identified purely from the mouse olfactory responses. Our findings suggest that Shapley fields may serve as a chemical feature-agnostic method for investigating olfactory perception. Author summary: The sense of smell remains poorly understood compared with other sensory systems in the mammalian brain. This is because there is little understanding of how specific chemical features drive olfactory perception and how these features are spatially organized within the olfactory bulb. We introduce a novel method for understanding the coding properties of olfactory processing in the mammalian brain. Using neural activity in the mouse olfactory bulb, we characterized the non-random organization of chemical sensitivity across the surface of the olfactory bulb. We also found that the chemical sensitivity of mouse olfactory glomeruli, small structures that receive input from smell receptor neurons, was well described by AI-generated chemical features derived from human perceptual data. Rather than forming a simple map dominated by one or two chemical properties, this organization involved at least three effective chemical-feature dimensions. The spatial pattern was weak and mosaic-like: glomeruli with related sensitivities tended to occur near one another, but their responses overlapped rather than forming sharply separated regions. Our results and methods provide a feature-agnostic framework for studying olfactory coding in the mammalian brain.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematical Sciences, University of Texas, El Paso, Texas, United States of America",
    "Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, Utah, United States of America",
    "Department of Mathematics, The Pennsylvania State University, University Park, Texas, Pennsylvania, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nikola Milićević"
    },
    {
      "affs": [
        1
      ],
      "name": "Shawn D. Burton"
    },
    {
      "affs": [
        2
      ],
      "name": "Matt Wachowiak"
    },
    {
      "name": "Vladimir Itskov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ac5gb9hkbmzqq.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014917",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014917",
  "id": "ac5gb9hkbmzqq",
  "issns": [
    "1553-7358"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014917",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "plos",
      "/Biology and life sciences/Anatomy/Brain/Olfactory bulb"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Sensory perception"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Sensory perception"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Perception/Sensory perception"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Brain/Olfactory bulb"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Odorants"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Algebra/Linear algebra/Vector spaces"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Cluster analysis/Hierarchical clustering"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Perception/Sensory perception"
    ]
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  "openalex": null,
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014917&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/ac5gb9hkbmzqq.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:20:11Z",
      "native_id": "10.1371/journal.pcbi.1014917",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014917"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
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    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Shapley fields reveal mosaic chemotopic organization in the mouse Olfactory bulb across diverse chemical feature sets",
  "title_text": "Shapley fields reveal mosaic chemotopic organization in the mouse Olfactory bulb across diverse chemical feature sets",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:20:43Z",
  "url": "https://research.draconitic.com/paper/ac5gb9hkbmzqq",
  "venue": "PLOS Computational Biology",
  "versions": []
}