Version history

{
  "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",
  "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"
  },
  "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_refs": [
    "paper:paper-733c0535ffcd"
  ],
  "source_span": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.",
  "study_system": "computational model, neuromorphic hardware",
  "evidence_refs": [
    {
      "ref": "paper:paper-733c0535ffcd"
    }
  ],
  "section_title": "Brain Region and Layer Context: Beyond Primary Sensory Cortex",
  "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"
  },
  "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"
}