{
  "annotation_id": "a312c300-1984-4933-b3fe-17aa7c7e56cc",
  "date_created": "2026-03-02T18:00:43.213000Z",
  "date_modified": "2026-03-02T18:00:43.213000Z",
  "file_hash": "ef77224efd2dce9ed4e0acbc1c67bea91d133f6276135415f705702868903b6b",
  "private": false,
  "record": {
    "abstract": "The major problem in information theoretic analysis of neural responses and\nother biological data is the reliable estimation of entropy--like quantities\nfrom small samples. We apply a recently introduced Bayesian entropy estimator\nto synthetic data inspired by experiments, and to real experimental spike\ntrains. The estimator performs admirably even very deep in the undersampled\nregime, where other techniques fail. This opens new possibilities for the\ninformation theoretic analysis of experiments, and may be of general interest\nas an example of learning from limited data.",
    "arxiv_id": "physics/0306063",
    "authors": [
      "Ilya Nemenman",
      "William Bialek",
      "Rob de Ruyter van Steveninck"
    ],
    "categories": [
      "physics.data-an",
      "physics.bio-ph",
      "q-bio.NC",
      "q-bio.QM"
    ],
    "doi": "10.1103/PhysRevE.69.056111",
    "journal_ref": "Phys. Rev. E 69, 056111 (2004)",
    "title": "Entropy and information in neural spike trains: Progress on the sampling   problem",
    "url": "https://arxiv.org/abs/physics/0306063"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6ef2fc2b-5720-425f-9ae3-03347ecb6c81",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}