Version history

{
  "scope": "Monkey PFC + RNNs (cross-species synthesis)",
  "claim_text": "In a probabilistic reversal-learning task, prefrontal-cortex population activity (monkey) and matched recurrent-neural-network models encode reversal probability in a stable, stationary state consistent with a line-attractor model, then transition during the trial into separable non-stationary trajectories — a synthesis result that anchors line-attractor accounts of recurrent cortical computation but is derived from primate, not mouse cortex.",
  "raw_fields": {
    "n": 0,
    "doi": "10.7554/elife.103660",
    "claim": "In a probabilistic reversal-learning task, prefrontal-cortex population activity (monkey) and matched recurrent-neural-network models encode reversal probability in a stable, stationary state consistent with a line-attractor model, then transition during the trial into separable non-stationary trajectories — a synthesis result that anchors line-attractor accounts of recurrent cortical computation but is derived from primate, not mouse cortex.",
    "cite_key": "Kim2025b",
    "evidence": "Monkey PFC recordings + RNN match; perturbation of reversal-probability subspace in RNN biased choices — functional significance.",
    "effect_size": "stationary line-attractor state at trial start; separable non-stationary trajectories during trial",
    "text_access": "fulltext",
    "study_system": "Monkey PFC + RNNs (cross-species synthesis)",
    "argument_role": "supporting",
    "replication_status": "cross-species-theory-data",
    "claim_source_sentence": "We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable, and consistent with the attractor dynamics.",
    "source_provenance_status": "ok",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "The reversal probability activity at the start of a trial was stationary, stable, and consistent with the attractor dynamics."
  },
  "section_id": "section_16",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_16_evidence_package.json",
  "effect_size": "stationary line-attractor state at trial start; separable non-stationary trajectories during trial",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-16-synthesis",
  "source_kind": "review_finding",
  "source_path": "evidence/section_16_evidence_package.json",
  "source_refs": [
    "paper:paper-bd6ab3ad8754"
  ],
  "source_span": "We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable, and consistent with the attractor dynamics.",
  "study_system": "Monkey PFC + RNNs (cross-species synthesis)",
  "evidence_refs": [
    {
      "ref": "paper:paper-bd6ab3ad8754"
    }
  ],
  "section_title": "16. Synthesis — which computational claims the mouse-cortex E→E empirical record actually supports, where the bottleneck observations are, and what an inhibition-free, single-species, basic-research analytic framing misses",
  "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": "Monkey PFC recordings + RNN match; perturbation of reversal-probability subspace in RNN biased choices — functional significance.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "cross-species-theory-data",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-16-synthesis",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_16_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}