{
  "annotation_id": "368790d2-7e0d-49a2-8230-c11962326ece",
  "date_created": "2026-03-02T18:01:35.410000Z",
  "date_modified": "2026-03-02T18:01:35.410000Z",
  "file_hash": "5a9b3fb6e03cbf00fd7ebd376b72c4cb5c8294e5b93f865437717168c6862acc",
  "private": false,
  "record": {
    "abstract": "We study the dynamical states that emerge in a small-world network of\nrecurrently coupled excitable neurons through both numerical and analytical\nmethods. These dynamics depend in large part on the fraction of long-range\nconnections or `short-cuts\u0027 and the delay in the neuronal interactions.\nPersistent activity arises for a small fraction of `short-cuts\u0027, while a\ntransition to failure occurs at a critical value of the `short-cut\u0027 density.\nThe persistent activity consists of multi-stable periodic attractors, the\nnumber of which is at least on the order of the number of neurons in the\nnetwork. For long enough delays, network activity at high `short-cut\u0027 densities\nis shown to exhibit exceedingly long chaotic transients whose failure-times\naveraged over many network configurations follow a stretched exponential. We\nshow how this functional form arises in the ensemble-averaged activity if each\nnetwork realization has a characteristic failure-time which is exponentially\ndistributed.",
    "arxiv_id": "q-bio/0701047",
    "authors": [
      "Hermann Riecke",
      "Alex Roxin",
      "Santiago Madruga",
      "Sara A. Solla"
    ],
    "categories": [
      "q-bio.NC",
      "cond-mat.dis-nn",
      "nlin.CD",
      "physics.bio-ph"
    ],
    "doi": "10.1063/1.2743611",
    "title": "Many Attractors, Long Chaotic Transients, and Failure in Small-World   Networks of Excitable Neurons",
    "url": "https://arxiv.org/abs/q-bio/0701047"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c66edf4d-0a0f-47dd-b001-8400349927b3",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}