Version history

{
  "scope": "visual cortex; computational model; Neuron",
  "claim_text": "A wealth of integrative properties, including the above, emerge robustly from four cortical circuit properties: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent exci…",
  "raw_fields": {
    "n": null,
    "doi": "10.1016/j.neuron.2014.12.026",
    "claim": "A wealth of integrative properties, including the above, emerge robustly from four cortical circuit properties: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent exci…",
    "cite_key": "Rubin2015",
    "evidence": "Neurons in sensory cortex integrate multiple influences to parse objects and support perception. Across multiple cortical areas, integration is characterized by two neuronal response properties: (1) surround suppression--modulatory contextual stimuli suppress responses to driving stimuli; and (2) \"normalization\"--responses to multiple driving stimuli add sublinearly. These depend on input strength: for weak driving stimuli, contextual influences facilitate or more weakly suppress and summation b...",
    "effect_size": null,
    "text_access": "fulltext",
    "study_system": "visual cortex; computational model; Neuron",
    "argument_role": "supporting",
    "replication_status": "single_study",
    "claim_source_sentence": "A wealth of integrative properties, including the above, emerge robustly from four cortical circuit properties: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; and (4) simple spatial properties of intracortical connections.",
    "source_provenance_status": "ok",
    "replication_evidence_dois": [],
    "claim_rewritten_from_source": true,
    "effect_size_source_sentence": null
  },
  "section_id": "section_09",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "effect_size": null,
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-09-amplification-isn",
  "source_kind": "review_finding",
  "source_path": "evidence/section_09_evidence_package.json",
  "source_refs": [
    "paper:paper-e15053dfa039"
  ],
  "source_span": "A wealth of integrative properties, including the above, emerge robustly from four cortical circuit properties: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; and (4) simple spatial properties of intracortical connections.",
  "study_system": "visual cortex; computational model; Neuron",
  "evidence_refs": [
    {
      "ref": "paper:paper-e15053dfa039"
    }
  ],
  "section_title": "9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation",
  "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": "Neurons in sensory cortex integrate multiple influences to parse objects and support perception. Across multiple cortical areas, integration is characterized by two neuronal response properties: (1) surround suppression--modulatory contextual stimuli suppress responses to driving stimuli; and (2) \"normalization\"--responses to multiple driving stimuli add sublinearly. These depend on input strength: for weak driving stimuli, contextual influences facilitate or more weakly suppress and summation b...",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "single_study",
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  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}