Version history

{
  "scope": "SSN model + reanalysis of multi-area cortical variability data (includes mouse/cat/monkey)",
  "claim_text": "In sensory cortex, stimulus-driven quenching of correlated variability is best explained by a stochastic stabilized supralinear network (SSN) operating in a 'loosely balanced' regime: as stimulus drive increases, supralinear neurons strengthen effective recurrent connectivity and shift the balance from amplification of variability to inhibitory suppression — a direct prediction of strong recurrent E-I cortical coupling.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1016/j.neuron.2018.04.017",
    "claim": "In sensory cortex, stimulus-driven quenching of correlated variability is best explained by a stochastic stabilized supralinear network (SSN) operating in a 'loosely balanced' regime: as stimulus drive increases, supralinear neurons strengthen effective recurrent connectivity and shift the balance from amplification of variability to inhibitory suppression — a direct prediction of strong recurrent E-I cortical coupling.",
    "cite_key": "Hennequin2018",
    "evidence": "Theoretical analysis of stochastic SSN; comparison to spatial patterns and fast temporal dynamics of cortical variability suppression from prior + new data analyses across sensory areas.",
    "effect_size": "qualitative — SSN uniquely accounts for spatial patterns and fast temporal dynamics of variability suppression",
    "text_access": "abstract_only",
    "study_system": "SSN model + reanalysis of multi-area cortical variability data (includes mouse/cat/monkey)",
    "argument_role": "supporting",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic 'stabilized supralinear network'), best explains these modulations.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [
      "10.7554/eLife.54875"
    ],
    "effect_size_source_sentence": "Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression."
  },
  "section_id": "section_09",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "effect_size": "qualitative — SSN uniquely accounts for spatial patterns and fast temporal dynamics of variability suppression",
  "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-818c0c4864de"
  ],
  "source_span": "Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic 'stabilized supralinear network'), best explains these modulations.",
  "study_system": "SSN model + reanalysis of multi-area cortical variability data (includes mouse/cat/monkey)",
  "evidence_refs": [
    {
      "ref": "paper:paper-818c0c4864de"
    }
  ],
  "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": "Theoretical analysis of stochastic SSN; comparison to spatial patterns and fast temporal dynamics of cortical variability suppression from prior + new data analyses across sensory areas.",
  "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"
}