Version history

{
  "scope": "spiking and reduced bump-attractor models with heterogeneous E→E coupling",
  "claim_text": "Spatial heterogeneity in recurrent excitatory coupling converts a continuous attractor into a discrete set of stable persistent states, reducing diffusion of working-memory representations at the cost of coarse-graining the stimulus space.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1523/jneurosci.1641-13.2013",
    "claim": "Spatial heterogeneity in recurrent excitatory coupling converts a continuous attractor into a discrete set of stable persistent states, reducing diffusion of working-memory representations at the cost of coarse-graining the stimulus space.",
    "cite_key": "Kilpatrick2013a",
    "evidence": "Spiking network plus reduced potential-well analysis of a parametric working-memory network with heterogeneous recurrent excitation; storage error is minimized at intermediate heterogeneity.",
    "effect_size": "qualitative",
    "text_access": "abstract_only",
    "study_system": "spiking and reduced bump-attractor models with heterogeneous E→E coupling",
    "argument_role": "supporting",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "Studying both a spiking network and a simplified potential well model, we show that spatially heterogeneous excitatory coupling stabilizes a discrete number of persistent states, reducing the diffusion of persistent activity over the network.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [
      "10.1093/cercor/10.9.910",
      "10.1162/0899766054026660"
    ],
    "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",
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    "paper:paper-805a32157eb2"
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  "source_span": "Studying both a spiking network and a simplified potential well model, we show that spatially heterogeneous excitatory coupling stabilizes a discrete number of persistent states, reducing the diffusion of persistent activity over the network.",
  "study_system": "spiking and reduced bump-attractor models with heterogeneous E→E coupling",
  "evidence_refs": [
    {
      "ref": "paper:paper-805a32157eb2"
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  ],
  "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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    "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."
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    "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
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  "evidence_summary": "Spiking network plus reduced potential-well analysis of a parametric working-memory network with heterogeneous recurrent excitation; storage error is minimized at intermediate heterogeneity.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "independently_replicated",
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}