{
  "annotation_id": "b9aa097b-d004-498c-8fe5-f5c77c0a88a0",
  "date_created": "2026-02-17T05:53:08.711000Z",
  "date_modified": "2026-02-17T05:53:08.711000Z",
  "file_hash": "c75ff3dcb954f0fa76ccee0cd8df006420b1251c38b9064b0285ae5c4af2d490",
  "private": false,
  "record": {
    "abstract": "The human brain\u0027s computational prowess emerges not despite but because of its inherent \"non-ideal factors\"-noise, heterogeneity, structural irregularities, decentralized plasticity, systemic errors, and chaotic dynamics-challenging classical neuroscience\u0027s idealized models. These traits, long dismissed as flaws, are evolutionary adaptations that endow the brain with robustness, creativity, and adaptability. Classical frameworks falter under the brain\u0027s complexity: simulating 86 billion neurons and 100 trillion synapses is intractable, stochastic neurotransmitter release confounds signal interpretation, and the absence of global idealized models invalidates deterministic learning frameworks. Technological gaps further obscure whole-brain dynamics, revealing a disconnect between biological reality and computational abstraction.",
    "arxiv_id": "2601.06531",
    "authors": [
      "Da-Zheng Feng",
      "Hao-Xuan Du"
    ],
    "categories": [
      "q-bio.NC"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Learning Principles for Overcoming Non-ideal Factors in Brain",
    "url": "https://arxiv.org/abs/2601.06531",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a9b92c82-d436-4fb5-a85a-0f19dc191056",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}