Version history

{
  "scope": "cortex/HC",
  "claim_text": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.",
  "raw_fields": {
    "n": "",
    "doi": "10.1073/pnas.2504164122",
    "claim": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.",
    "title": null,
    "cite_key": "Iqbal2025",
    "evidence": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.. Understanding the computational principles of the brain and translating them into neuromorphic hardware and modern deep learning architectures is critical for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally constrained, biophysically realistic model of neocortical microcircuits in the mouse primary visual cortex (layers 2 to 3) to examine how four major interneuron classes-Parvalbumin, Somatostatin, vasoactive intestinal peptide, and L",
    "effect_size": "20%",
    "text_access": "fulltext",
    "study_system": "cortex/HC",
    "_source_cluster": "cluster_05_synaptic_connectivity",
    "replication_status": "single",
    "_source_cluster_index": 181,
    "claim_source_sentence": null,
    "replication_evidence_dois": []
  },
  "section_id": "section_06",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewVIP/blob/95e761177f7d2ec565983d3307c14ec238f9677c/evidence/section_06_evidence_package.json",
  "effect_size": "20%",
  "review_repo": "ComputationalReviewVIP",
  "section_ref": "wiki_page:computationalreviewvip-06-synaptic-properties",
  "source_kind": "review_finding",
  "source_path": "evidence/section_06_evidence_package.json",
  "source_refs": [
    "paper:paper-733c0535ffcd"
  ],
  "source_span": "",
  "study_system": "cortex/HC",
  "evidence_refs": [
    {
      "ref": "paper:paper-733c0535ffcd"
    }
  ],
  "section_title": "Synaptic Properties and Connectivity",
  "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": "95e761177f7d2ec565983d3307c14ec238f9677c",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewVIP"
  },
  "evidence_summary": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.. Understanding the computational principles of the brain and translating them into neuromorphic hardware and modern deep learning architectures is critical for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally constrained, biophysically realistic model of neocortical microcircuits in the mouse primary visual cortex (layers 2 to 3) to examine how four major interneuron classes-Parvalbumin, Somatostatin, vasoactive intestinal peptide, and L",
  "review_bundle_ref": "analysis_bundle:ab-2ce40c33e827",
  "replication_status": "single",
  "review_package_ref": "analysis_bundle:ab-2ce40c33e827",
  "source_artifact_ref": "wiki_page:computationalreviewvip-06-synaptic-properties",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewVIP/blob/95e761177f7d2ec565983d3307c14ec238f9677c/evidence/section_06_evidence_package.json",
  "commit_sha": "95e761177f7d2ec565983d3307c14ec238f9677c",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewVIP"
}