Networks of strongly and randomly connected excitatory neurons, stabilized by p…

Networks of strongly and randomly connected excitatory neurons, stabilized by precisely tuned inhibition, transiently amplify specific activity states and can execute complex multidimensional movement patterns — establishing inhibitory stabilization of strong recurrent E→E coupling as an organizational principle of cortex.

{
  "n": 0,
  "doi": "10.1016/j.neuron.2014.04.045",
  "claim": "Networks of strongly and randomly connected excitatory neurons, stabilized by precisely tuned inhibition, transiently amplify specific activity states and can execute complex multidimensional movement patterns — establishing inhibitory stabilization of strong recurrent E→E coupling as an organizational principle of cortex.",
  "cite_key": "Hennequin2014",
  "evidence": "Theoretical analysis (control theory + linear systems) and simulation of strongly recurrent excitatory networks with tuned inhibition; benchmarked against motor cortex preparatory and movement-related dynamics.",
  "effect_size": "qualitative — transient amplification and reliable movement execution emerge",
  "text_access": "abstract_only",
  "study_system": "Motor cortex transient dynamics model (theoretical/simulation; mouse/monkey data benchmark)",
  "argument_role": "supporting",
  "replication_status": "independently_replicated",
  "claim_source_sentence": "Here we introduce a class of cortical architectures with strong and random excitatory recurrence that is stabilized by intricate, fine-tuned inhibition, optimized from a control theory perspective.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [
    "10.1016/j.neuron.2009.02.005",
    "10.1016/j.neuron.2023.11.005"
  ],
  "effect_size_source_sentence": "Such networks transiently amplify specific activity states and can be used to reliably execute multidimensional movement patterns."
}

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[
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    "ref": "paper:paper-1e48f47c8295"
  }
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  "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."
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  "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}