Details

scope
Mouse V1 recurrent network model (theoretical/simulation, calibrated to experiments)
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
study_system
Mouse V1 recurrent network model (theoretical/simulation, calibrated to experiments)
section_title
9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation
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
Raw fields (6)
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."
}
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.
evidence_refs
[
  {
    "ref": "paper:paper-dc0786d96e71"
  }
]
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"
}

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