Version history

{
  "scope": "2D continuous attractor neural-field network model",
  "claim_text": "Classical continuous bump-attractor models suffer from biologically unrealistic fine tuning of recurrent connectivity and from a stereotyped bump shape that cannot encode representational quality; a 2D Amari-type network with locally balanced E/I feedback removes both limitations.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1007/s11571-023-09979-3",
    "claim": "Classical continuous bump-attractor models suffer from biologically unrealistic fine tuning of recurrent connectivity and from a stereotyped bump shape that cannot encode representational quality; a 2D Amari-type network with locally balanced E/I feedback removes both limitations.",
    "cite_key": "Wojtak2024",
    "evidence": "Two-dimensional Amari-type neural field model with locally balanced excitatory–inhibitory feedback loop, analyzed for stability and tested numerically against connectivity perturbations.",
    "effect_size": "qualitative",
    "text_access": "abstract_only",
    "study_system": "2D continuous attractor neural-field network model",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning.",
    "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-68171c2580ca"
  ],
  "source_span": "Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning.",
  "study_system": "2D continuous attractor neural-field network model",
  "evidence_refs": [
    {
      "ref": "paper:paper-68171c2580ca"
    }
  ],
  "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": "Two-dimensional Amari-type neural field model with locally balanced excitatory–inhibitory feedback loop, analyzed for stability and tested numerically against connectivity perturbations.",
  "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"
}