Version history

{
  "scope": "Mouse visual cortex; two-photon imaging + MICrONS connectome; deep-learning foundation model",
  "claim_text": "A foundation deep-learning model trained on two-photon calcium imaging of mouse visual cortices generalizes to new mice with minimal training, predicts responses to held-out stimulus classes, and accurately classifies anatomically defined excitatory cell types in the MICrONS dataset of more than 70,000 neurons.",
  "raw_fields": {
    "n": 70000,
    "doi": "10.1038/s41586-025-08829-y",
    "claim": "A foundation deep-learning model trained on two-photon calcium imaging of mouse visual cortices generalizes to new mice with minimal training, predicts responses to held-out stimulus classes, and accurately classifies anatomically defined excitatory cell types in the MICrONS dataset of more than 70,000 neurons.",
    "cite_key": "Wang2025b",
    "evidence": "Demonstrates that emerging foundation models can predict functional and anatomical properties of mouse cortex from large-scale 2P data — directly addresses cluster_14 'near-horizon tools' topic.",
    "effect_size": "MICrONS dataset >70,000 neurons; model trained on eight mice",
    "text_access": "fulltext",
    "study_system": "Mouse visual cortex; two-photon imaging + MICrONS connectome; deep-learning foundation model",
    "argument_role": "supporting",
    "replication_status": "single-study",
    "claim_source_sentence": "In the Machine Intelligence from Cortical Networks (MICrONS) dataset 2 , which contains functional recordings and nanoscale anatomy of more than 70,000 neurons, our model accurately classified anatomically defined types of excitatory neurons.",
    "source_provenance_status": "ok",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "With a subset of these data, we trained a deep neural network on recordings from eight mice, producing a ‘foundation core’ that captured shared latent representations and predicted neuronal responses across mice and cortical areas."
  },
  "section_id": "section_15",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_15_evidence_package.json",
  "effect_size": "MICrONS dataset >70,000 neurons; model trained on eight mice",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-15-methods-limits",
  "source_kind": "review_finding",
  "source_path": "evidence/section_15_evidence_package.json",
  "source_refs": [
    "paper:paper-a802f6ac77d4"
  ],
  "source_span": "In the Machine Intelligence from Cortical Networks (MICrONS) dataset 2 , which contains functional recordings and nanoscale anatomy of more than 70,000 neurons, our model accurately classified anatomically defined types of excitatory neurons.",
  "study_system": "Mouse visual cortex; two-photon imaging + MICrONS connectome; deep-learning foundation model",
  "evidence_refs": [
    {
      "ref": "paper:paper-a802f6ac77d4"
    }
  ],
  "section_title": "15. Methodological limits and emerging tools — what current mouse-cortex tools cannot yet measure about E→E recurrence (subthreshold network activity, fast plasticity in vivo, millimetre-scale dynamic connectomes), and what is on the near horizon",
  "source_policy": {
    "mode": "public_source_pointer_with_short_context",
    "notes": [
      "Local review repositories are read-only inputs.",
      "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
    ],
    "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
  },
  "evidence_summary": "Demonstrates that emerging foundation models can predict functional and anatomical properties of mouse cortex from large-scale 2P data — directly addresses cluster_14 'near-horizon tools' topic.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "single-study",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-15-methods-limits",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_15_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}