{
  "annotation_id": "f71ecb82-4c13-418f-89b6-d31e6a99c4a5",
  "date_created": "2026-03-02T18:00:43.213000Z",
  "date_modified": "2026-03-02T18:00:43.213000Z",
  "file_hash": "9af5f609bd44727a364099b80d47cc801a22bd834a2327bbfa252ebfa340e7f5",
  "private": false,
  "record": {
    "abstract": "This paper deals with the problem of extracting the activity of individual\nneurons from multi-electrode recordings. Important aspects of this work are: 1)\nthe sorting is done in two stages - a statistical model of the spikes from\ndifferent cells is built and only then are occurrences of these spikes in the\ndata detected by scanning through the original data, 2) the spike sorting is\ndone in the frequency domain, 3) strict statistical tests are applied to\ndetermine if and how a spike should be classiffed, 4) the statistical model for\ndetecting overlaping spike events is proposed, 5) slow dynamics of spike shapes\nare tracked during long experiments. Results from the application of these\ntechniques to data collected from the escape response system of the American\ncockroach, Periplaneta americana, are presented.",
    "arxiv_id": "physics/0306056",
    "authors": [
      "Dima Rinberg",
      "William Bialek",
      "Hanan Davidowitz",
      "Naftali Tishby"
    ],
    "categories": [
      "physics.data-an",
      "physics.bio-ph",
      "q-bio.QM"
    ],
    "title": "Spike sorting in the frequency domain with overlap detection",
    "url": "https://arxiv.org/abs/physics/0306056"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4ede1586-e714-47ff-8dfa-6f7d00dbd29c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}