Version history

{
  "scope": "Mouse V1 (visual cortex); layer L2/3",
  "claim_text": "In mouse V1 L2/3, the long-tailed distribution of excitatory connection weights is explained by a rule whereby neurons with highly correlated responses form strong connections and uncorrelated neurons connect rarely and weakly.",
  "raw_fields": {
    "n": 1,
    "doi": "10.1038/nature14182",
    "claim": "In mouse V1 L2/3, the long-tailed distribution of excitatory connection weights is explained by a rule whereby neurons with highly correlated responses form strong connections and uncorrelated neurons connect rarely and weakly.",
    "cite_key": "Cossell2015",
    "evidence": "Paired EPSP vs. response-correlation analysis of mouse V1 L2/3 pyramidal pairs after in vivo imaging.",
    "effect_size": "Cochran–Armitage trend p = 8.2 × 10⁻⁶ (across binned similarity)",
    "text_access": "fulltext",
    "study_system": "Mouse V1 (visual cortex); layer L2/3",
    "argument_role": "supporting",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "Together, these data suggest that the long-tailed distribution of cortical connection weights arises from a simple rule: neurons with highly correlated responses form strong connections, and neurons with uncorrelated responses connect rarely, and only weakly.",
    "source_provenance_status": "ok",
    "replication_evidence_dois": [
      "10.1038/nature09880",
      "10.1038/s41586-025-08840-3"
    ],
    "effect_size_source_sentence": "Neurons with more similar responses were much more likely to connect (= 8.2 × 10, Cochran–Armitage test for trend;), consistent with previous observations."
  },
  "section_id": "section_06",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_06_evidence_package.json",
  "effect_size": "Cochran–Armitage trend p = 8.2 × 10⁻⁶ (across binned similarity)",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-06-connectomic-micons",
  "source_kind": "review_finding",
  "source_path": "evidence/section_06_evidence_package.json",
  "source_refs": [
    "paper:paper-c4ad527c9bfb"
  ],
  "source_span": "Together, these data suggest that the long-tailed distribution of cortical connection weights arises from a simple rule: neurons with highly correlated responses form strong connections, and neurons with uncorrelated responses connect rarely, and only weakly.",
  "study_system": "Mouse V1 (visual cortex); layer L2/3",
  "evidence_refs": [
    {
      "ref": "paper:paper-c4ad527c9bfb"
    }
  ],
  "section_title": "6. Connectomic resolution — MICrONS mouse V1/HVA millimetre-scale EM volume; pyramidal-pyramidal wiring statistics derived from it; reconciliation with paired-recording estimates",
  "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": "Paired EPSP vs. response-correlation analysis of mouse V1 L2/3 pyramidal pairs after in vivo imaging.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "independently_replicated",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-06-connectomic-micons",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_06_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}