Version history

{
  "scope": "loosely balanced supralinear excitatory-inhibitory recurrent network (SSSN) compared against monkey and rodent sensory-cortex variability data",
  "claim_text": "Stimulus-dependent quenching of correlated variability across cortex is best explained by fluctuations about a single stimulus-driven attractor in a loosely balanced supralinear excitatory–inhibitory network (stochastic SSSN), rather than by multi-stable attractors or chaotic dynamics, with stimulus drive strengthening effective recurrence and shifting balance toward inhibitory feedback.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1016/j.neuron.2018.04.017",
    "claim": "Stimulus-dependent quenching of correlated variability across cortex is best explained by fluctuations about a single stimulus-driven attractor in a loosely balanced supralinear excitatory–inhibitory network (stochastic SSSN), rather than by multi-stable attractors or chaotic dynamics, with stimulus drive strengthening effective recurrence and shifting balance toward inhibitory feedback.",
    "cite_key": "Hennequin2018",
    "evidence": "Provides an alternative single-attractor regime that competes with multi-attractor models for explaining mouse-cortex variability statistics.",
    "effect_size": "qualitative",
    "text_access": "abstract_only",
    "study_system": "loosely balanced supralinear excitatory-inhibitory recurrent network (SSSN) compared against monkey and rodent sensory-cortex variability data",
    "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",
      "10.1523/eneuro.0459-24.2025"
    ],
    "effect_size_source_sentence": null
  },
  "section_id": "section_13",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "effect_size": "qualitative",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-13-attractor-network-models",
  "source_kind": "review_finding",
  "source_path": "evidence/section_13_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": "loosely balanced supralinear excitatory-inhibitory recurrent network (SSSN) compared against monkey and rodent sensory-cortex variability data",
  "evidence_refs": [
    {
      "ref": "paper:paper-818c0c4864de"
    }
  ],
  "section_title": "13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides",
  "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": "Provides an alternative single-attractor regime that competes with multi-attractor models for explaining mouse-cortex variability statistics.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "independently_replicated",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-13-attractor-network-models",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}