{
  "annotation_id": "4838cb1a-0e17-4fe3-9f7a-5aa1c312f09a",
  "date_created": "2026-03-02T18:02:48.001000Z",
  "date_modified": "2026-03-02T18:02:48.001000Z",
  "file_hash": "7978bbec80ae785650ec369157307dce887936b1ac410a82a9d1026c007c03a0",
  "private": false,
  "record": {
    "abstract": "Inspired by the dissipative quantum model of brain, we model the states of\nneural nets in terms of collective modes by the help of the formalism of\nQuantum Field Theory. We exhibit an explicit neural net model which allows to\nmemorize a sequence of several informations without reciprocal destructive\ninterference, namely we solve the overprinting problem in such a way last\nregistered information does not destroy the ones previously registered.\nMoreover, the net is able to recall not only the last registered information in\nthe sequence, but also anyone of those previously registered.",
    "arxiv_id": "quant-ph/9912070",
    "authors": [
      "Eliano Pessa",
      "Giuseppe Vitiello"
    ],
    "categories": [
      "quant-ph",
      "cond-mat.other",
      "hep-th",
      "physics.bio-ph",
      "q-bio.OT"
    ],
    "journal_ref": "Bioelectrochem.Bioenerg 48 (1999) 339-342",
    "title": "Quantum dissipation and neural net dynamics",
    "url": "https://arxiv.org/abs/quant-ph/9912070"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "90496b22-7ffc-4e86-adf5-a1baeea21fe2",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}