The stabilized supralinear network (SSN) framework — recurrent E-I network with…

The stabilized supralinear network (SSN) framework — recurrent E-I network with supralinear power-law neuronal I/O — exhibits inhibition-stabilization with strong inputs, producing a supralinear-to-sublinear transition in input summation that recurrent E→E gain produces in cortex.

{
  "n": 0,
  "doi": "10.1162/neco_a_00472",
  "claim": "The stabilized supralinear network (SSN) framework — recurrent E-I network with supralinear power-law neuronal I/O — exhibits inhibition-stabilization with strong inputs, producing a supralinear-to-sublinear transition in input summation that recurrent E→E gain produces in cortex.",
  "cite_key": "Ahmadian2013",
  "evidence": "Analytic and numerical analysis of two-population E-I rate-model network with power-law input-output functions; conditions for dynamic stabilization derived.",
  "effect_size": "qualitative — wide-range supralinear→sublinear summation transition with increasing input strength",
  "text_access": "abstract_only",
  "study_system": "SSN model relevant to cortex (cat/mouse data benchmark)",
  "argument_role": "supporting",
  "replication_status": "independently_replicated",
  "claim_source_sentence": "We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [
    "10.1073/pnas.1700080115",
    "10.1523/ENEURO.0459-24.2025"
  ],
  "effect_size_source_sentence": "For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs."
}

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  "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
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