Details
- scope
- Elife
- claim_text
- Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules.
- section_id
- section_04
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_04_evidence_package.json
- effect_size
- review_repo
- ComputationalReviewLoops
- section_ref
- wiki_page:computationalreviewloops-04
- source_kind
- review_finding
- source_path
- evidence/section_04_evidence_package.json
- source_span
- We show that this pathological behavior is reversed if, after action selection, opponent dSPNs and iSPNs receive correlated efferent input encoding the animal’s selected action.
- study_system
- Elife
- section_title
- Striatal Output Pathways: Direct, Indirect, and Beyond
- evidence_summary
- review_bundle_ref
- analysis_bundle:ab-d49e54403ef9
- replication_status
- replication_unknown
- review_package_ref
- analysis_bundle:ab-d49e54403ef9
- source_artifact_ref
- wiki_page:computationalreviewloops-04
- origin_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_04_evidence_package.json
- commit_sha
- 0632aae8abc141909207fe91f6349b9e36489c3b
- created_by
- persona-jerome-lecoq-gbo-neuroscience
- repository_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewLoops
Raw fields (4)
- raw_fields
{ "n": 0, "doi": "10.7554/elife.101747", "claim": "Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules.", "cite_key": "Lindsey2025", "evidence": "", "effect_size": "", "text_access": "fulltext", "study_system": "Elife", "source_cluster_id": "cluster_04", "replication_status": "replication_unknown", "claim_source_sentence": "We show that this pathological behavior is reversed if, after action selection, opponent dSPNs and iSPNs receive correlated efferent input encoding the animal’s selected action.", "replication_evidence_dois": [], "effect_size_source_sentence": "While we illustrated this in a task with sequential trials for visualization purposes, this non-interference enables learning based on delayed reward and efferent feedback from past actions even as the selection of subsequent actions unfolds." }- source_refs
[ "paper:paper-64428af7e294" ]
- evidence_refs
[ { "ref": "paper:paper-64428af7e294" } ]- 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": "0632aae8abc141909207fe91f6349b9e36489c3b", "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops" }