Version history

{
  "scope": "SSN model relevant to cortex (cat/mouse data benchmark)",
  "claim_text": "The stabilized supralinear network (SSN) framework — recurrent E-I network with supralinear power-law neuronal I/O — exhibits inhibition-stabilization with strong inputs, producing a supralinear-to-sublinear transition in input summation that recurrent E→E gain produces in cortex.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1162/neco_a_00472",
    "claim": "The stabilized supralinear network (SSN) framework — recurrent E-I network with supralinear power-law neuronal I/O — exhibits inhibition-stabilization with strong inputs, producing a supralinear-to-sublinear transition in input summation that recurrent E→E gain produces in cortex.",
    "cite_key": "Ahmadian2013",
    "evidence": "Analytic and numerical analysis of two-population E-I rate-model network with power-law input-output functions; conditions for dynamic stabilization derived.",
    "effect_size": "qualitative — wide-range supralinear→sublinear summation transition with increasing input strength",
    "text_access": "abstract_only",
    "study_system": "SSN model relevant to cortex (cat/mouse data benchmark)",
    "argument_role": "supporting",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [
      "10.1073/pnas.1700080115",
      "10.1523/ENEURO.0459-24.2025"
    ],
    "effect_size_source_sentence": "For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs."
  },
  "section_id": "section_09",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "effect_size": "qualitative — wide-range supralinear→sublinear summation transition with increasing input strength",
  "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-534291e9b1f8"
  ],
  "source_span": "We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong.",
  "study_system": "SSN model relevant to cortex (cat/mouse data benchmark)",
  "evidence_refs": [
    {
      "ref": "paper:paper-534291e9b1f8"
    }
  ],
  "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"
  },
  "evidence_summary": "Analytic and numerical analysis of two-population E-I rate-model network with power-law input-output functions; conditions for dynamic stabilization derived.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "independently_replicated",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "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"
}