Version history

{
  "scope": "computational network model",
  "claim_text": "Modulation of metastable ensemble dynamics explains the inverted-U relationship between tone discriminability and arousal in auditory cortex",
  "raw_fields": {
    "n": null,
    "doi": "10.1101/2024.04.04.588209",
    "claim": "Modulation of metastable ensemble dynamics explains the inverted-U relationship between tone discriminability and arousal in auditory cortex",
    "cite_key": "Papadopoulos2024",
    "evidence": "Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of thi",
    "effect_size": "qualitative",
    "text_access": "abstract_only",
    "study_system": "computational network model",
    "argument_role": "supporting",
    "replication_status": "single_study",
    "claim_source_sentence": "Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal).",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal)."
  },
  "section_id": "section_13",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "effect_size": "qualitative",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-13-attractor-network-models",
  "source_kind": "review_finding",
  "source_path": "evidence/section_13_evidence_package.json",
  "source_refs": [
    "paper:paper-ddc92a8df885"
  ],
  "source_span": "Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal).",
  "study_system": "computational network model",
  "evidence_refs": [
    {
      "ref": "paper:paper-ddc92a8df885"
    }
  ],
  "section_title": "13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides",
  "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"
  },
  "evidence_summary": "Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of thi",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "single_study",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-13-attractor-network-models",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}