Version history

{
  "scope": "coupled bump-attractor stochastic neural-field model (cortex-cortex)",
  "claim_text": "Reciprocal long-range coupling between two bump-attractor areas always reduces the variability of bump position when coupling is sufficiently strong, even when the two areas have very different intrinsic noise.",
  "raw_fields": {
    "n": 0,
    "doi": "10.3389/fncom.2013.00082",
    "claim": "Reciprocal long-range coupling between two bump-attractor areas always reduces the variability of bump position when coupling is sufficiently strong, even when the two areas have very different intrinsic noise.",
    "cite_key": "Kilpatrick2013b",
    "evidence": "Stochastic neural-field model of two coupled bump-attractor areas, analyzed with a small-noise expansion yielding a multivariate Ornstein–Uhlenbeck approximation.",
    "effect_size": "qualitative",
    "text_access": "abstract_only",
    "study_system": "coupled bump-attractor stochastic neural-field model (cortex-cortex)",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "This shows reciprocal coupling between areas can always reduce variability, if sufficiently strong, even if one area contains much more noise than the other.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "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-503446332e9d"
  ],
  "source_span": "This shows reciprocal coupling between areas can always reduce variability, if sufficiently strong, even if one area contains much more noise than the other.",
  "study_system": "coupled bump-attractor stochastic neural-field model (cortex-cortex)",
  "evidence_refs": [
    {
      "ref": "paper:paper-503446332e9d"
    }
  ],
  "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": {
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    "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": "Stochastic neural-field model of two coupled bump-attractor areas, analyzed with a small-noise expansion yielding a multivariate Ornstein–Uhlenbeck approximation.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "replication_unknown",
  "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"
}