Details
- scope
- recurrent spiking cortical network model, fit to monkey PFC vibrotactile WM
- claim_text
- A line-attractor recurrent network for parametric (graded) working memory requires fine-tuning that corresponds to precise alignment of cusps in the network's bifurcation diagram.
- 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 show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network.
- study_system
- recurrent spiking cortical network model, fit to monkey PFC vibrotactile WM
- 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
- Spiking recurrent cortical-network model designed to fit Romo et al. vibrotactile working-memory data from monkey prefrontal cortex; analytical link to a continuous family of fixed points.
- review_bundle_ref
- analysis_bundle:ab-d9c479db9be9
- replication_status
- independently_replicated
- 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 (4)
- raw_fields
{ "n": 0, "doi": "10.1093/cercor/bhg101", "claim": "A line-attractor recurrent network for parametric (graded) working memory requires fine-tuning that corresponds to precise alignment of cusps in the network's bifurcation diagram.", "cite_key": "Miller2003", "evidence": "Spiking recurrent cortical-network model designed to fit Romo et al. vibrotactile working-memory data from monkey prefrontal cortex; analytical link to a continuous family of fixed points.", "effect_size": "qualitative", "text_access": "abstract_only", "study_system": "recurrent spiking cortical network model, fit to monkey PFC vibrotactile WM", "argument_role": "supporting", "replication_status": "independently_replicated", "claim_source_sentence": "We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network.", "source_provenance_status": "non_substring_match", "replication_evidence_dois": [ "10.1016/j.pneurobio.2013.02.002", "10.1162/0899766054026660" ], "effect_size_source_sentence": null }- source_refs
[ "paper:paper-d71c2717ce37" ]
- evidence_refs
[ { "ref": "paper:paper-d71c2717ce37" } ]- 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" }