{
  "abstract": "Human visual cortex is organized into dorsal, lateral, and ventral streams. A long-standing hypothesis is that the functional organization into streams emerged to support distinct visual behaviors. Here, we compare neural network-based computational models against a massive fMRI dataset to investigate why visual streams emerge. We find that a self-supervised Topographic Deep Artificial Neural Network (TDANN), which encourages nearby units to respond similarly, better captures brain responses, as well as spatial segregation and functional differentiation across streams, than DANN models trained for stream-specific visual behaviors. These findings challenge the prevailing view that streams evolved to separately support different behaviors and suggest instead the possibility that functional organization can arise from a single principle: learning generally useful visual representations subject to local spatial constraints. Visual streams are often explained as pathways optimized for different behaviors. Here, the authors show that brain-like stream organization can instead arise from a single self-supervised objective under local spatial constraints.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Stanford University, Stanford, CA, USA. finzidawn@gmail.com",
    "Department of Psychology, Stanford University, Stanford, CA, USA. finzidawn@gmail.com",
    "Neurosciences Graduate Program, Stanford University, Stanford, CA, USA",
    "Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA",
    "Department of Computer Science, Stanford University, Stanford, CA, USA",
    "Department of Psychology, Stanford University, Stanford, CA, USA",
    "Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Dawn Finzi",
      "orcid": "0000-0003-0636-7375"
    },
    {
      "affs": [
        2
      ],
      "name": "Eshed Margalit",
      "orcid": "0000-0003-0841-7444"
    },
    {
      "affs": [
        3
      ],
      "name": "Kendrick Kay",
      "orcid": "0000-0001-6604-9155"
    },
    {
      "affs": [
        4,
        5,
        6
      ],
      "name": "Daniel L. K. Yamins",
      "orcid": "0000-0001-6155-4523"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Kalanit Grill-spector A",
      "orcid": "0000-0002-5404-9606"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/17sez4m6w421x.bib",
  "cited_by": 13,
  "doi": "10.1038/s41467-026-76098-y",
  "fulltext_url": null,
  "id": "17sez4m6w421x",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-76098-y",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|Visual perception and processing mechanisms"
    ]
  ],
  "openalex": "W7202347126",
  "pdf_url": "https://www.nature.com/articles/s41467-026-76098-y_reference.pdf",
  "pmcid": "PMC13572372",
  "pmid": "42733086",
  "published": "2026-08-13",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/17sez4m6w421x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:53Z",
      "native_id": "W7202347126",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41467-026-76098-y"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Visual perception and processing mechanisms",
      "subfield": "neuroscience"
    }
  ],
  "title": "A single computational objective can produce specialization of streams in visual cortex",
  "title_text": "A single computational objective can produce specialization of streams in visual cortex",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:12Z",
  "url": "https://research.draconitic.com/paper/17sez4m6w421x",
  "venue": "Nature Communications",
  "versions": []
}