In realistic cortical network models, detecting an inhibition-stabilized (high-…

In realistic cortical network models, detecting an inhibition-stabilized (high-gain E→E) regime via paradoxical perturbation requires perturbing a large fraction of the inhibitory population — explaining why earlier small-perturbation experiments failed to find paradoxical effects despite cortex likely being ISN.

{
  "n": 0,
  "doi": "10.1523/jneurosci.0963-17.2017",
  "claim": "In realistic cortical network models, detecting an inhibition-stabilized (high-gain E→E) regime via paradoxical perturbation requires perturbing a large fraction of the inhibitory population — explaining why earlier small-perturbation experiments failed to find paradoxical effects despite cortex likely being ISN.",
  "cite_key": "Sadeh2017",
  "evidence": "Simulations of cortical networks of increasing realism with parametric variation of perturbation extent and inhibitory fraction; comparison to published optogenetic ISN experiments.",
  "effect_size": "qualitative — narrow-perturbation paradigms fail; large-fraction perturbation required",
  "text_access": "abstract_only",
  "study_system": "Cortical network simulations (data benchmarks from mouse V1 optogenetic ISN experiments)",
  "argument_role": "supporting",
  "replication_status": "independently_replicated",
  "claim_source_sentence": "Our models predict that a large proportion of the inhibitory network must be perturbed to reliably detect an ISN regime robustly in cortex.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [
    "10.7554/eLife.54875",
    "10.7554/eLife.49967"
  ],
  "effect_size_source_sentence": "Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex."
}

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