{
  "annotation_id": "5224d807-ebf2-4f4b-bb08-9e036be0d964",
  "date_created": "2026-03-02T18:01:21.780000Z",
  "date_modified": "2026-03-02T18:01:21.780000Z",
  "file_hash": "cd2ca89b4a407898efde8467b5cd0fea57980a5b8c6b3a0e10b7fe148eac2c22",
  "private": false,
  "record": {
    "abstract": "We model the dynamics of the leaky integrate-fire neuron under periodic\nstimulation as a Markov process with respect to the stimulus phase. This avoids\nthe unrealistic assumption of a stimulus reset after each spike made in earlier\nwork and thus solves the long-standing reset problem. The neuron exhibits\nstochastic resonance, both with respect to input noise intensity and stimulus\nfrequency. The latter resonance arises by matching the stimulus frequency to\nthe refractory time of the neuron. The Markov approach can be generalized to\nother periodically driven stochastic processes containing a reset mechanism.",
    "arxiv_id": "physics/9810019",
    "authors": [
      "Hans E. Plesser",
      "Theo Geisel"
    ],
    "categories": [
      "physics.bio-ph",
      "q-bio"
    ],
    "doi": "10.1103/PhysRevE.59.7008",
    "journal_ref": "Phys Rev E 59:7008-7017 (1999)",
    "title": "Markov analysis of stochastic resonance in a periodically driven   integrate-fire neuron",
    "url": "https://arxiv.org/abs/physics/9810019"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e2ef512a-f0d7-43ac-b331-1b9834d60633",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}