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{
  "tags": [
    "DeepInterpolation",
    "dimensionality",
    "covariance",
    "Marchenko-Pastur",
    "calcium imaging",
    "visual cortex"
  ],
  "text": "DeepInterpolation denoising systematically reduces the apparent dimensionality of neural population activity in mouse visual cortex calcium imaging data, as measured by the number of significant principal components above the Marchenko-Pastur noise floor. After DeepInterpolation, the covariance eigenspectrum shifts: the bulk noise floor collapses and a smaller number of supra-threshold eigenvalues survive, indicating that a portion of dimensions detected in raw data reflect independent pixel-level noise rather than true shared variance across neurons.",
  "links": {
    "source_papers": [
      "doi:10.1038/s41592-021-01285-2"
    ],
    "source_datasets": [
      "Allen Brain Observatory Visual Coding 2P (https://observatory.brain-map.org/visualcoding)"
    ],
    "supporting_figures": []
  },
  "local_id": "claim-di-dimensionality",
  "confidence": "moderate",
  "created_by": "persona-jerome-lecoq"
}