Version history

{
  "scope": "Theoretical model; biophysical simulations referenced to cat V1 data",
  "claim_text": "In recurrent cortical circuits with strong but balanced excitation and inhibition, 'balanced amplification' arises as a non-normal mechanism that selectively amplifies activity patterns without slowing dynamics — providing the theoretical framework for E→E recurrent gain in cortex.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1016/j.neuron.2009.02.005",
    "claim": "In recurrent cortical circuits with strong but balanced excitation and inhibition, 'balanced amplification' arises as a non-normal mechanism that selectively amplifies activity patterns without slowing dynamics — providing the theoretical framework for E→E recurrent gain in cortex.",
    "cite_key": "Murphy2009",
    "evidence": "Linear analysis of E/I recurrent network with Dale's law shows non-normal dynamics with hidden feedforward connectivity between activity patterns; biophysical model reproduces V1 ongoing activity statistics.",
    "effect_size": "qualitative — non-normal amplification arises in any balanced E/I recurrent network",
    "text_access": "fulltext",
    "study_system": "Theoretical model; biophysical simulations referenced to cat V1 data",
    "argument_role": "supporting",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "When excitation and inhibition are both strong but balanced, as is thought to be the case in cerebral cortex, balanced amplification arises: small patterned fluctuations of the difference between excitation and inhibition drive large patterned fluctuations of the sum.",
    "source_provenance_status": "ok",
    "replication_evidence_dois": [
      "10.1103/PhysRevE.86.011909",
      "10.1016/j.neuron.2018.02.031",
      "10.1016/j.neuron.2023.11.005"
    ],
    "effect_size_source_sentence": null
  },
  "section_id": "section_09",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "effect_size": "qualitative — non-normal amplification arises in any balanced E/I recurrent network",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-09-amplification-isn",
  "source_kind": "review_finding",
  "source_path": "evidence/section_09_evidence_package.json",
  "source_refs": [
    "paper:paper-73146022940d"
  ],
  "source_span": "When excitation and inhibition are both strong but balanced, as is thought to be the case in cerebral cortex, balanced amplification arises: small patterned fluctuations of the difference between excitation and inhibition drive large patterned fluctuations of the sum.",
  "study_system": "Theoretical model; biophysical simulations referenced to cat V1 data",
  "evidence_refs": [
    {
      "ref": "paper:paper-73146022940d"
    }
  ],
  "section_title": "9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation",
  "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"
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  "evidence_summary": "Linear analysis of E/I recurrent network with Dale's law shows non-normal dynamics with hidden feedforward connectivity between activity patterns; biophysical model reproduces V1 ongoing activity statistics.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "independently_replicated",
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  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-09-amplification-isn",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}