Version history

{
  "scope": "Mouse V1 recurrent network model (theoretical/simulation, calibrated to experiments)",
  "claim_text": "Reconciling feature-specific suppression observed with single-neuron stimulation in mouse V1 with feature-specific E→E connectivity requires strong AND functionally specific E-I connectivity — neither inhibition dominance nor broad inhibition alone suffices.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1073/pnas.2004568117",
    "claim": "Reconciling feature-specific suppression observed with single-neuron stimulation in mouse V1 with feature-specific E→E connectivity requires strong AND functionally specific E-I connectivity — neither inhibition dominance nor broad inhibition alone suffices.",
    "cite_key": "Sadeh2020a",
    "evidence": "Large-scale recurrent network simulations and mathematical analysis of single-neuron perturbations across connectivity regimes; calibrated to mouse V1 perturbation data.",
    "effect_size": "qualitative — strong, feature-specific E-I connectivity required; emerges population-level response gain nonlinearities",
    "text_access": "abstract_only",
    "study_system": "Mouse V1 recurrent network model (theoretical/simulation, calibrated to experiments)",
    "argument_role": "supporting",
    "replication_status": "within_lab",
    "claim_source_sentence": "Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [
      "10.1038/s41586-019-0997-6"
    ],
    "effect_size_source_sentence": "Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level."
  },
  "section_id": "section_09",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "effect_size": "qualitative — strong, feature-specific E-I connectivity required; emerges population-level response gain nonlinearities",
  "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-dc0786d96e71"
  ],
  "source_span": "Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents.",
  "study_system": "Mouse V1 recurrent network model (theoretical/simulation, calibrated to experiments)",
  "evidence_refs": [
    {
      "ref": "paper:paper-dc0786d96e71"
    }
  ],
  "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": "Large-scale recurrent network simulations and mathematical analysis of single-neuron perturbations across connectivity regimes; calibrated to mouse V1 perturbation data.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "within_lab",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-09-amplification-isn",
  "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"
}