Version history

{
  "scope": "Motor cortex transient dynamics model (theoretical/simulation; mouse/monkey data benchmark)",
  "claim_text": "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.",
  "raw_fields": {
    "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."
  },
  "section_id": "section_09",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "effect_size": "qualitative — transient amplification and reliable movement execution emerge",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-09-amplification-isn",
  "source_kind": "review_finding",
  "source_path": "evidence/section_09_evidence_package.json",
  "source_refs": [
    "paper:paper-1e48f47c8295"
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  "source_span": "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.",
  "study_system": "Motor cortex transient dynamics model (theoretical/simulation; mouse/monkey data benchmark)",
  "evidence_refs": [
    {
      "ref": "paper:paper-1e48f47c8295"
    }
  ],
  "section_title": "9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation",
  "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"
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  "evidence_summary": "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.",
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  "replication_status": "independently_replicated",
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  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-09-amplification-isn",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}