Version history

{
  "scope": "Awake mouse V1 LFP and multiunit recordings during visual oddball",
  "claim_text": "In an oddball paradigm, mouse V1 deviance detection emerges in supragranular layers (L2/3) with a 150–230 ms latency, after basic adaptation in L4 (~50 ms) — matching predictive coding's separation of feedforward error and feedback prediction streams.",
  "raw_fields": {
    "n": null,
    "doi": "10.1093/cercor/bhad215",
    "claim": "In an oddball paradigm, mouse V1 deviance detection emerges in supragranular layers (L2/3) with a 150–230 ms latency, after basic adaptation in L4 (~50 ms) — matching predictive coding's separation of feedforward error and feedback prediction streams.",
    "cite_key": "Gallimore2023a",
    "evidence": "16-channel LFP + multiunit recordings across V1 layers in awake mice during a visual oddball; spectral signatures (delta/theta and high-gamma increase in L2/3, beta decrease in L1).",
    "effect_size": "DD signal: 150-230 ms in L2/3; adaptation in L4 at 50 ms; spectral shifts in delta/theta (2-7 Hz), high-gamma (70-80 Hz), beta (26-36 Hz)",
    "text_access": "abstract_only",
    "study_system": "Awake mouse V1 LFP and multiunit recordings during visual oddball",
    "argument_role": "supporting",
    "replication_status": "independently_replicated",
    "claim_source_sentence": "Multiunit activity and current source density profiles showed that although basic adaptation to redundant stimuli was present early (50 ms) in layer 4 responses, DD emerged later (150-230 ms) in supragranular layers (L2/3).",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [
      "10.1016/j.celrep.2016.06.037",
      "10.1093/cercor/bhad163"
    ],
    "effect_size_source_sentence": "This DD signal coincided with increased delta/theta (2-7 Hz) and high-gamma (70-80 Hz) oscillations in L2/3 and decreased beta oscillations (26-36 Hz) in L1."
  },
  "section_id": "section_14",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json",
  "effect_size": "DD signal: 150-230 ms in L2/3; adaptation in L4 at 50 ms; spectral shifts in delta/theta (2-7 Hz), high-gamma (70-80 Hz), beta (26-36 Hz)",
  "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-c0f0ae0b432b"
  ],
  "source_span": "Multiunit activity and current source density profiles showed that although basic adaptation to redundant stimuli was present early (50 ms) in layer 4 responses, DD emerged later (150-230 ms) in supragranular layers (L2/3).",
  "study_system": "Awake mouse V1 LFP and multiunit recordings during visual oddball",
  "evidence_refs": [
    {
      "ref": "paper:paper-c0f0ae0b432b"
    }
  ],
  "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": "16-channel LFP + multiunit recordings across V1 layers in awake mice during a visual oddball; spectral signatures (delta/theta and high-gamma increase in L2/3, beta decrease in L1).",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "independently_replicated",
  "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"
}