{
  "annotation_id": "53c21602-aaac-485f-9f37-f8d419a50978",
  "date_created": "2026-03-02T18:01:35.310000Z",
  "date_modified": "2026-03-02T18:01:35.310000Z",
  "file_hash": "f36dc8283a68b65264beb85388382862b7c10804e7a5b23c7eaf13620578dd93",
  "private": false,
  "record": {
    "abstract": "We study a deterministic dynamics with two time scales in a continuous state\nattractor network. To the usual (fast) relaxation dynamics towards point\nattractors (``patterns\u0027\u0027) we add a slow coupling dynamics that makes the\nvisited patterns to loose stability leading to an itinerant behavior in the\nform of punctuated equilibria. One finds that the transition frequency matrix\nbetween patterns shows non-trivial statistical properties in the chaotic\nitinerant regime. We show that mixture input patterns can be temporally\nsegmented by the itinerant dynamics. The viability of a combinatorial\nspatio-temporal neural code is also demonstrated.",
    "arxiv_id": "q-bio/0605010",
    "authors": [
      "Juliana R. Dias",
      "Rodrigo F. Oliveira",
      "Osame Kinouchi"
    ],
    "categories": [
      "q-bio.NC"
    ],
    "doi": "10.1016/j.physd.2007.06.021",
    "journal_ref": "Physica D: Nonlinear Phenomena, Volume 237, Issue 1, p. 1-5 (2008)",
    "title": "Chaotic itinerancy, temporal segmentation and spatio-temporal   combinatorial codes",
    "url": "https://arxiv.org/abs/q-bio/0605010"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5612dfdb-d2b7-44ab-b2ca-7a0052db1100",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}