{
  "annotation_id": "a0f99961-cf72-4cde-8a78-d69800b791de",
  "date_created": "2026-03-02T18:01:31.670000Z",
  "date_modified": "2026-03-02T18:01:31.670000Z",
  "file_hash": "89a432fe4379082c7e4c5889f4235ad4ef3f591a8b5704575bcf375bc1b4b52e",
  "private": false,
  "record": {
    "abstract": "The theory of computational complexity is used to underpin a recent model of\nneocortical sensory processing. We argue that encoding into reconstruction\nnetworks is appealing for communicating agents using Hebbian learning and\nworking on hard combinatorial problems, which are easy to verify. Computational\ndefinition of the concept of intelligence is provided. Simulations illustrate\nthe idea.",
    "arxiv_id": "q-bio/0403022",
    "authors": [
      "Andras Lorincz"
    ],
    "categories": [
      "q-bio.NC",
      "cs.AI",
      "cs.CC",
      "nlin.AO"
    ],
    "title": "Intelligent encoding and economical communication in the visual stream",
    "url": "https://arxiv.org/abs/q-bio/0403022"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "32672367-9be8-4392-8485-1fcbcdf1f51c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}