{
  "annotation_id": "c50a37a9-3571-4bdf-b7dc-7fcde59a0c03",
  "date_created": "2026-03-02T18:00:29.222000Z",
  "date_modified": "2026-03-02T18:00:29.222000Z",
  "file_hash": "22e5b7f2479bb6b48c419badc12390fa869bb1a702ea1efde7f27e8427f932e2",
  "private": false,
  "record": {
    "abstract": "Sampling considerations limit the experimental conditions under which\ninformation theoretic analyses of neurophysiological data yield reliable\nresults. We develop a procedure for computing the full temporal entropy and\ninformation of ensembles of neural spike trains, which performs reliably for\nlimited samples of data. This approach also yields insight upon the role of\ncorrelations between spikes in temporal coding mechanisms. The method, when\napplied to recordings from complex cells of the monkey primary visual cortex,\nresults in lower RMS error information estimates in comparison to a `brute\nforce\u0027 approach.",
    "arxiv_id": "physics/0001006",
    "authors": [
      "Simon R. Schultz",
      "Stefano Panzeri"
    ],
    "categories": [
      "physics.bio-ph",
      "cond-mat.dis-nn",
      "q-bio.NC"
    ],
    "doi": "10.1103/PhysRevLett.86.5823",
    "title": "Temporal correlations and neural spike train entropy",
    "url": "https://arxiv.org/abs/physics/0001006"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "46e5d4f7-65d9-4b37-9a7a-673847fe9154",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}