Version history

{
  "scope": "Rodent PFC, macaque motor cortex, RNNs",
  "claim_text": "Tensor component analysis identifies low-dimensional neural-population factors at single-trial resolution across multiple timescales, applicable to mouse PFC, monkey M1 and trained networks.",
  "raw_fields": {
    "n": null,
    "doi": "10.1016/j.neuron.2018.05.015",
    "claim": "Tensor component analysis identifies low-dimensional neural-population factors at single-trial resolution across multiple timescales, applicable to mouse PFC, monkey M1 and trained networks.",
    "cite_key": "Williams2018",
    "evidence": "TCA applied to artificial RNNs, rodent PFC calcium imaging during maze navigation, and macaque M1 BMI recordings.",
    "effect_size": null,
    "text_access": "abstract_only",
    "study_system": "Rodent PFC, macaque motor cortex, RNNs",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state.",
    "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",
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  "source_path": "evidence/section_14_evidence_package.json",
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    "paper:paper-f716c4e9a30d"
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  "source_span": "We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive stat...",
  "study_system": "Rodent PFC, macaque motor cortex, RNNs",
  "evidence_refs": [
    {
      "ref": "paper:paper-f716c4e9a30d"
    }
  ],
  "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": "TCA applied to artificial RNNs, rodent PFC calcium imaging during maze navigation, and macaque M1 BMI recordings.",
  "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"
}