{
  "annotation_id": "52931e7c-5ce2-4bb5-98ee-54f0fcd5574a",
  "date_created": "2026-03-02T18:01:32.335000Z",
  "date_modified": "2026-03-02T18:01:32.335000Z",
  "file_hash": "91d9c91c2869bfbf3379d05d372526b10f9e0d77437d607375537fc19ea6e3bb",
  "private": false,
  "record": {
    "abstract": "We present for mental processes the program of mathematical mapping which has\nbeen successfully realized for physical processes. We emphasize that our\nproject is not about mathematical simulation of brain\u0027s functioning as a\ncomplex physical system, i.e., mapping of physical and chemical processes in\nthe brain on mathematical spaces. The project is about mapping of purely mental\nprocesses on mathematical spaces. We present various arguments -- philosophic,\nmathematical, information, and neurophysiological -- in favor of the $p$-adic\nmodel of mental space. $p$-adic spaces have structures of hierarchic trees and\nin our model such a tree hierarchy is considered as an image of neuronal\nhierarchy. Hierarchic neural pathways are considered as fundamental units of\ninformation processing. As neural pathways can go through whole body, the\nmental space is produced by the whole neural system. Finally, we develop\nProbabilistic Neural Pathway Model in that Mental States are represented by\nprobability distributions on mental space.",
    "arxiv_id": "q-bio/0408022",
    "authors": [
      "Andrei Khrennikov"
    ],
    "categories": [
      "q-bio.NC",
      "cond-mat.dis-nn",
      "math.DS",
      "quant-ph"
    ],
    "journal_ref": "J. Theor. Biology, 231, 597-613 (2004)",
    "title": "Probabilistic pathway representation of cognitive information",
    "url": "https://arxiv.org/abs/q-bio/0408022"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d104c777-79dd-40cd-9920-33d99d1747c7",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}