Version history

{
  "scope": "Bilateral motor and premotor cortex during naturalistic movement",
  "claim_text": "Cross-population linear dynamical models trained with a prioritized objective can isolate cross-region motor-cortex interactions from within-region dynamics during a naturalistic task.",
  "raw_fields": {
    "n": null,
    "doi": "10.1088/1741-2552/ade569",
    "claim": "Cross-population linear dynamical models trained with a prioritized objective can isolate cross-region motor-cortex interactions from within-region dynamics during a naturalistic task.",
    "cite_key": "Jha2025",
    "evidence": "CroP-LDM benchmarked against static and dynamic latent-variable methods on bilateral motor + premotor cortical data.",
    "effect_size": null,
    "text_access": "abstract_only",
    "study_system": "Bilateral motor and premotor cortex during naturalistic movement",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "Second, using multi-regional bilateral motor and premotor cortical recordings during a naturalistic movement task, we demonstrate that CroP-LDM better learns cross-population dynamics compared to recent static and dynamic methods, even when using a low dimensionality.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": null
  },
  "section_id": "section_14",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json",
  "effect_size": null,
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-14-predictive-coding",
  "source_kind": "review_finding",
  "source_path": "evidence/section_14_evidence_package.json",
  "source_refs": [
    "paper:paper-c7edae965800"
  ],
  "source_span": "Second, using multi-regional bilateral motor and premotor cortical recordings during a naturalistic movement task, we demonstrate that CroP-LDM better learns cross-population dynamics compared to recent static and dynamic methods, even when using a low dimensionality.",
  "study_system": "Bilateral motor and premotor cortex during naturalistic movement",
  "evidence_refs": [
    {
      "ref": "paper:paper-c7edae965800"
    }
  ],
  "section_title": "14. Predictive-coding and dynamical-systems accounts — the role of recurrent excitatory feedback in error signalling, state estimation, and reservoir computing, evaluated against mouse data",
  "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": "CroP-LDM benchmarked against static and dynamic latent-variable methods on bilateral motor + premotor cortical data.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "replication_unknown",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-14-predictive-coding",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}