{
  "abstract": "In many neural populations, the computationally relevant signals are posited to be a set of \"latent factors\"-signals shared across many individual neurons. A given brain area may perform many computations, each associated with distinct factors, which can together compose an overall action. The methods for uncovering such structure typically require supervision, which can limit the discovery of novel aspects of activity. Here, we introduce sparse component analysis (SCA), an unsupervised approach. SCA facilitated surprisingly clear parcellations of neural activity across a range of behaviors, when seeking both linear and nonlinear embeddings. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA revealed both simple and unexpected instances where the overall population response was built compositionally from sets of factors with distinct computational roles.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA",
    "Department of Neurology, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA",
    "Department of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA",
    "Department of Physics, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA",
    "Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Allen Institute for Neural Dynamics, Allen Institute, Seattle, WA, USA; Department of Neurobiology and Biophysics, University of Washington, Seattle, WA, USA",
    "Zuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Department of Statistics, Columbia University, New York, NY, USA",
    "Department of Neuroscience, Columbia University Medical Center, New York, NY, USA; Zuckerman Institute, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA. Electronic address: mc3502@columbia.edu",
    "Department of Neurology, Northwestern University, Chicago, IL, USA; Department of Computer Science, Northwestern University, Evanston, IL, USA; National Institute for Theory and Mathematics in Biology, Chicago, IL, USA. Electronic address: j-glaser@northwestern.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Andrew J. Zimnik",
      "orcid": "0000-0002-8313-2476"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinyue An"
    },
    {
      "affs": [
        2
      ],
      "name": "K. Cora Ames",
      "orcid": "0000-0003-0657-0162"
    },
    {
      "affs": [
        1
      ],
      "name": "Andrew Ulmer"
    },
    {
      "affs": [
        0
      ],
      "name": "Antonio H. Lara"
    },
    {
      "affs": [
        0
      ],
      "name": "Abigail A. Russo",
      "orcid": "0000-0001-7751-3355"
    },
    {
      "affs": [
        3
      ],
      "name": "Vladislav Susoy",
      "orcid": "0000-0002-0669-0087"
    },
    {
      "affs": [
        4
      ],
      "name": "Laura N. Driscoll",
      "orcid": "0000-0001-5247-1857"
    },
    {
      "affs": [
        5
      ],
      "name": "John Patrick Cunningham",
      "orcid": "0000-0001-9125-3027"
    },
    {
      "affs": [
        5
      ],
      "name": "Liam Paninski",
      "orcid": "0000-0002-0276-7032"
    },
    {
      "affs": [
        6
      ],
      "name": "Mark M. Churchland",
      "orcid": "0000-0001-9123-6526"
    },
    {
      "affs": [
        7
      ],
      "name": "Joshua I. Glaser",
      "orcid": "0000-0003-2906-115X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2qv3qaxnc1xa0.bib",
  "cited_by": 14,
  "doi": "10.1016/j.neuron.2026.05.022",
  "fulltext_url": null,
  "id": "2qv3qaxnc1xa0",
  "issns": [
    "0896-6273",
    "1097-4199"
  ],
  "landing_url": "https://doi.org/10.1016/j.neuron.2026.05.022",
  "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|Neural dynamics and brain function"
    ]
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  "openalex": "W7167081437",
  "pdf_url": null,
  "pmcid": "PMC10871230",
  "pmid": "38370650",
  "published": "2026-07-01",
  "publisher": "Cell Press",
  "ris": "https://research.draconitic.com/paper/2qv3qaxnc1xa0.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:11Z",
      "native_id": "W7167081437",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.neuron.2026.05.022"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Neural dynamics and brain function",
      "subfield": "neuroscience"
    }
  ],
  "title": "Sparse component analysis: A method that uncovers separable computations within neural population activity",
  "title_text": "Sparse component analysis: A method that uncovers separable computations within neural population activity",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:12Z",
  "url": "https://research.draconitic.com/paper/2qv3qaxnc1xa0",
  "venue": "Neuron",
  "versions": []
}