Version history

{
  "scope": "continuous attractor neural network model with separate E and I populations",
  "claim_text": "A bump-attractor network that simultaneously encodes location and certainty (amplitude) of a memorized stimulus requires precisely tuned E/I balance so that inhibition cancels background excitation.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1007/s10827-013-0486-0",
    "claim": "A bump-attractor network that simultaneously encodes location and certainty (amplitude) of a memorized stimulus requires precisely tuned E/I balance so that inhibition cancels background excitation.",
    "cite_key": "Carroll2014",
    "evidence": "Constructive model of a two-dimensional bump attractor (position × amplitude) in a network with separate E and I populations; derives the E/I tuning requirement.",
    "effect_size": "qualitative",
    "text_access": "abstract_only",
    "study_system": "continuous attractor neural network model with separate E and I populations",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "In networks with separate excitatory and inhibitory populations, generating bumps with a continuum of possible amplitudes, requires tuning the strength of inhibition to precisely cancel background excitation.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": null
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  "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",
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  "source_span": "In networks with separate excitatory and inhibitory populations, generating bumps with a continuum of possible amplitudes, requires tuning the strength of inhibition to precisely cancel background excitation.",
  "study_system": "continuous attractor neural network model with separate E and I populations",
  "evidence_refs": [
    {
      "ref": "paper:paper-c2eb477f7f17"
    }
  ],
  "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",
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      "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."
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  "evidence_summary": "Constructive model of a two-dimensional bump attractor (position × amplitude) in a network with separate E and I populations; derives the E/I tuning requirement.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "replication_unknown",
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  "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",
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}