In sensory cortex, stimulus-driven quenching of correlated variability is best…

In sensory cortex, stimulus-driven quenching of correlated variability is best explained by a stochastic stabilized supralinear network (SSN) operating in a 'loosely balanced' regime: as stimulus drive increases, supralinear neurons strengthen effective recurrent connectivity and shift the balance from amplification of variability to inhibitory suppression — a direct prediction of strong recurrent E-I cortical coupling.

{
  "n": 0,
  "doi": "10.1016/j.neuron.2018.04.017",
  "claim": "In sensory cortex, stimulus-driven quenching of correlated variability is best explained by a stochastic stabilized supralinear network (SSN) operating in a 'loosely balanced' regime: as stimulus drive increases, supralinear neurons strengthen effective recurrent connectivity and shift the balance from amplification of variability to inhibitory suppression — a direct prediction of strong recurrent E-I cortical coupling.",
  "cite_key": "Hennequin2018",
  "evidence": "Theoretical analysis of stochastic SSN; comparison to spatial patterns and fast temporal dynamics of cortical variability suppression from prior + new data analyses across sensory areas.",
  "effect_size": "qualitative — SSN uniquely accounts for spatial patterns and fast temporal dynamics of variability suppression",
  "text_access": "abstract_only",
  "study_system": "SSN model + reanalysis of multi-area cortical variability data (includes mouse/cat/monkey)",
  "argument_role": "supporting",
  "replication_status": "independently_replicated",
  "claim_source_sentence": "Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic 'stabilized supralinear network'), best explains these modulations.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [
    "10.7554/eLife.54875"
  ],
  "effect_size_source_sentence": "Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression."
}

[
  "paper:paper-818c0c4864de"
]

Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic 'stabilized supralinear network'), best explains these modulations.

[
  {
    "ref": "paper:paper-818c0c4864de"
  }
]

{
  "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"
}