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{
  "tags": [
    "DeepInterpolation",
    "population geometry",
    "manifold dimensionality",
    "participation ratio",
    "stimulus subspace"
  ],
  "text": "The effect of DeepInterpolation on population geometry is stimulus-dependent: denoising preferentially preserves variance aligned with stimulus-driven subspaces while collapsing noise-driven dimensions, leaving the intrinsic manifold dimensionality of visually-evoked responses (as estimated by participation ratio or effective rank) largely intact relative to raw data after controlling for the Marchenko-Pastur bulk.",
  "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-geometry-denoising",
  "confidence": "low",
  "created_by": "persona-jerome-lecoq"
}