Version history

{
  "scope": "Theory of neuronal perturbome in cortical networks.",
  "claim_text": "Theoretical work directly bearing on E→E recurrent connectivity inferred from V1 perturbation experiments; supports feature-specific E→E plus configured inhibition.",
  "raw_fields": {
    "n": null,
    "doi": "10.1073/pnas.2004568117",
    "claim": "Theoretical work directly bearing on E→E recurrent connectivity inferred from V1 perturbation experiments; supports feature-specific E→E plus configured inhibition.",
    "cite_key": "Sadeh2020a",
    "evidence": "To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. 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. Such networks had a higher capacity to encode and d",
    "effect_size": null,
    "text_access": "abstract_only",
    "study_system": "Theory of neuronal perturbome in cortical networks.",
    "argument_role": "supporting",
    "replication_status": null,
    "claim_source_sentence": "Modelling of single-neuron perturbations in large recurrent networks suggests that neither inhibition dominance nor broad inhibition alone are sufficient to explain feature-specific suppression observed between mouse V1 excitatory neurons despite highly specific E→E connectivity; strong specific E→E coupling plus broad/specific inhibition is required.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": null
  },
  "section_id": "section_03",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_03_evidence_package.json",
  "effect_size": null,
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-03-paired-recording",
  "source_kind": "review_finding",
  "source_path": "evidence/section_03_evidence_package.json",
  "source_refs": [
    "paper:paper-dc0786d96e71"
  ],
  "source_span": "Modelling of single-neuron perturbations in large recurrent networks suggests that neither inhibition dominance nor broad inhibition alone are sufficient to explain feature-specific suppression observed between mouse V1 excitatory neurons despite highly specific E→E connectivity; strong specific E→E coupling plus broad/specific inhibition is required.",
  "study_system": "Theory of neuronal perturbome in cortical networks.",
  "evidence_refs": [
    {
      "ref": "paper:paper-dc0786d96e71"
    }
  ],
  "section_title": "3. Paired-recording evidence in mouse — connection probabilities and synaptic strengths between pyramidal cells within a column, layer-by-layer (Lefort, Petersen, Adesnik, Feldmeyer, Markram-style work in mouse)",
  "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": "To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. 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. Such networks had a higher capacity to encode and d",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "unevaluated",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-03-paired-recording",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_03_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}