Version history

{
  "scope": "Detailed biophysical model of mouse primary motor cortex",
  "claim_text": "A multiscale, biophysically detailed model of mouse M1 with cell-type and laminar specificity predicts behavioral-state-dependent firing rates and LFP and yields low-dimensional latent dynamics.",
  "raw_fields": {
    "n": null,
    "doi": "10.1016/j.celrep.2023.112574",
    "claim": "A multiscale, biophysically detailed model of mouse M1 with cell-type and laminar specificity predicts behavioral-state-dependent firing rates and LFP and yields low-dimensional latent dynamics.",
    "cite_key": "DuraBernal2023",
    "evidence": "Mouse M1 model with >10,000 neurons and 30 million synapses, constrained by experimental connectivity and biophysics.",
    "effect_size": ">10,000 neurons and ~30 million synapses",
    "text_access": "abstract_only",
    "study_system": "Detailed biophysical model of mouse primary motor cortex",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation).",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses."
  },
  "section_id": "section_14",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json",
  "effect_size": ">10,000 neurons and ~30 million synapses",
  "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-39da47e849b2"
  ],
  "source_span": "The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation).",
  "study_system": "Detailed biophysical model of mouse primary motor cortex",
  "evidence_refs": [
    {
      "ref": "paper:paper-39da47e849b2"
    }
  ],
  "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": {
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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."
    ],
    "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
  },
  "evidence_summary": "Mouse M1 model with >10,000 neurons and 30 million synapses, constrained by experimental connectivity and biophysics.",
  "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"
}