Details
- 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.
- section_id
- section_14
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json
- review_repo
- ComputationalReviewRecurrence
- section_ref
- wiki_page:computationalreviewrecurrence-14-predictive-coding
- source_kind
- review_finding
- source_path
- evidence/section_14_evidence_package.json
- study_system
- Rodent PFC, macaque motor cortex, RNNs
- 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
- 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
Raw fields (5)
- 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 }- source_refs
[ "paper:paper-f716c4e9a30d" ]
- 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...
- evidence_refs
[ { "ref": "paper:paper-f716c4e9a30d" } ]- 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" }