Details

scope
monkey prefrontal cortex recordings + recurrent neural network models
section_id
section_13
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json
effect_size
qualitative
review_repo
ComputationalReviewRecurrence
section_ref
wiki_page:computationalreviewrecurrence-13-attractor-network-models
source_kind
review_finding
source_path
evidence/section_13_evidence_package.json
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.
study_system
monkey prefrontal cortex recordings + recurrent neural network models
section_title
13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides
evidence_summary
Analysis of monkey PFC recordings and trained RNNs on a probabilistic reversal-learning task; subspace dynamics fit a line attractor at trial start, with predictive modeling of non-stationary dynamics.
review_bundle_ref
analysis_bundle:ab-d9c479db9be9
replication_status
replication_unknown
review_package_ref
analysis_bundle:ab-d9c479db9be9
source_artifact_ref
wiki_page:computationalreviewrecurrence-13-attractor-network-models
origin_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json
commit_sha
79ce062d54a924ce05953ec90aa9d26044d2b48f
created_by
persona-jerome-lecoq-gbo-neuroscience
repository_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (5)
claim_text
In monkey PFC during probabilistic reversal learning, a subspace encoding reversal probability shows reward-outcome integration consistent with a line-attractor model, with trial-start activity acting as a stable initial condition for non-stationary trial dynamics.
raw_fields
{
  "n": 0,
  "doi": "10.7554/elife.103660",
  "claim": "In monkey PFC during probabilistic reversal learning, a subspace encoding reversal probability shows reward-outcome integration consistent with a line-attractor model, with trial-start activity acting as a stable initial condition for non-stationary trial dynamics.",
  "cite_key": "Kim2025b",
  "evidence": "Analysis of monkey PFC recordings and trained RNNs on a probabilistic reversal-learning task; subspace dynamics fit a line attractor at trial start, with predictive modeling of non-stationary dynamics.",
  "effect_size": "qualitative",
  "text_access": "abstract_only",
  "study_system": "monkey prefrontal cortex recordings + recurrent neural network models",
  "argument_role": "supporting",
  "replication_status": "replication_unknown",
  "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.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [],
  "effect_size_source_sentence": null
}
source_refs
[
  "paper:paper-bd6ab3ad8754"
]
evidence_refs
[
  {
    "ref": "paper:paper-bd6ab3ad8754"
  }
]
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"
}

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