Details
- scope
- computational model, neuromorphic hardware
- claim_text
- Biologically grounded neocortical computational primitives based on PV and SST interneuron inhibitory motifs improve vision transformer performance on neuromorphic hardware
- section_id
- section_09_evidence_package
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewPV/blob/df9fc7e8d455b084152c9d713558dae0013cef21/evidence/section_09_evidence_package.json
- effect_size
- The sWTA filter boosted accuracy on unseen data by up to ~20% and reduced training compute by directing learning toward salient features, without additional data or architectural changes
- review_repo
- ComputationalReviewPV
- section_ref
- wiki_page:computationalreviewpv-09
- source_kind
- review_finding
- source_path
- evidence/section_09_evidence_package.json
- source_span
- Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.
- study_system
- computational model, neuromorphic hardware
- section_title
- Brain Region and Layer Context: Beyond Primary Sensory Cortex
- evidence_summary
- Biologically grounded neocortical computational primitives based on PV and SST interneuron inhibitory motifs improve vision transformer performance on neuromorphic hardware
- review_bundle_ref
- analysis_bundle:ab-e6261c8263e7
- replication_status
- replication_unknown
- review_package_ref
- analysis_bundle:ab-e6261c8263e7
- source_artifact_ref
- wiki_page:computationalreviewpv-09
- origin_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewPV/blob/df9fc7e8d455b084152c9d713558dae0013cef21/evidence/section_09_evidence_package.json
- commit_sha
- df9fc7e8d455b084152c9d713558dae0013cef21
- created_by
- persona-jerome-lecoq-gbo-neuroscience
- repository_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewPV
Raw fields (4)
- raw_fields
{ "n": 0, "doi": "10.1073/pnas.2504164122", "claim": "Biologically grounded neocortical computational primitives based on PV and SST interneuron inhibitory motifs improve vision transformer performance on neuromorphic hardware", "evidence": "Biologically grounded neocortical computational primitives based on PV and SST interneuron inhibitory motifs improve vision transformer performance on neuromorphic hardware", "effect_size": "The sWTA filter boosted accuracy on unseen data by up to ~20% and reduced training compute by directing learning toward salient features, without additional data or architectural changes", "text_access": "fulltext", "study_system": "computational model, neuromorphic hardware", "replication_status": "replication_unknown", "claim_source_sentence": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.", "replication_evidence_dois": [], "effect_size_source_sentence": "The sWTA filter boosted accuracy on unseen data by up to ~20% and reduced training compute by directing learning toward salient features, without additional data or architectural changes" }- source_refs
[ "paper:paper-733c0535ffcd" ]
- evidence_refs
[ { "ref": "paper:paper-733c0535ffcd" } ]- 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": "df9fc7e8d455b084152c9d713558dae0013cef21", "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewPV" }