Details

scope
spiking neural network model of L2/3 cortex, neuromorphic hardware (TrueNorth)
section_id
section_12
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewVIP/blob/95e761177f7d2ec565983d3307c14ec238f9677c/evidence/section_12_evidence_package.json
review_repo
ComputationalReviewVIP
section_ref
wiki_page:computationalreviewvip-12-computational-models
source_kind
review_finding
source_path
evidence/section_12_evidence_package.json
study_system
spiking neural network model of L2/3 cortex, neuromorphic hardware (TrueNorth)
section_title
Computational Models of VIP Circuit Function
evidence_summary
Authors built a 4-interneuron-type spiking network and embedded sWTA, gain control, disinhibition, and normalization motifs onto neuromorphic hardware to improve vision-transformer performance.
review_bundle_ref
analysis_bundle:ab-2ce40c33e827
replication_status
replicated_independent
review_package_ref
analysis_bundle:ab-2ce40c33e827
source_artifact_ref
wiki_page:computationalreviewvip-12-computational-models
origin_url
https://github.com/AllenNeuralDynamics/ComputationalReviewVIP/blob/95e761177f7d2ec565983d3307c14ec238f9677c/evidence/section_12_evidence_package.json
commit_sha
95e761177f7d2ec565983d3307c14ec238f9677c
created_by
persona-jerome-lecoq-gbo-neuroscience
repository_url
https://github.com/AllenNeuralDynamics/ComputationalReviewVIP
Raw fields (6)
claim_text
A biologically constrained spiking model of layer 2/3 incorporating four canonical interneuron classes (PV, SST, VIP, LAMP5) implements soft winner-take-all dynamics, with VIP transiently disinhibiting SST and LAMP5 providing global gain normalization.
raw_fields
{
  "n": null,
  "id": "cluster_11_finding_01",
  "doi": "10.1073/pnas.2504164122",
  "pmid": "41055996",
  "year": "2025",
  "claim": "A biologically constrained spiking model of layer 2/3 incorporating four canonical interneuron classes (PV, SST, VIP, LAMP5) implements soft winner-take-all dynamics, with VIP transiently disinhibiting SST and LAMP5 providing global gain normalization.",
  "pmcid": "PMC12541343",
  "title": "Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.",
  "authors": "Iqbal A, Mahmood H, Stuart GJ, Fishell G, Honnuraiah S.",
  "journal": "Proceedings of the National Academy of Sciences of the United States of America",
  "cite_key": "Iqbal2025",
  "evidence": "Authors built a 4-interneuron-type spiking network and embedded sWTA, gain control, disinhibition, and normalization motifs onto neuromorphic hardware to improve vision-transformer performance.",
  "effect_size": null,
  "text_access": "fulltext",
  "study_system": "spiking neural network model of L2/3 cortex, neuromorphic hardware (TrueNorth)",
  "_source_cluster": "cluster_11_computational_models",
  "replication_status": "replicated_independent",
  "_source_cluster_index": 0,
  "claim_source_sentence": "In sensory cortex, pyramidal neurons integrate bottom–up and top–down signals under the modulatory influence of key interneuron classes: parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP), and LAMP5-expressing neurogliaform cells each of which mediates distinct computational roles including gain control, disinhibition, and normalization respectively ( 6 – 12 ).",
  "replication_evidence_dois": [
    "10.1371/journal.pcbi.1013469",
    "10.1371/journal.pcbi.1012036",
    "10.1093/cercor/bhae378",
    "10.1007/s00422-021-00894-6",
    "10.7554/elife.77594"
  ],
  "effect_size_source_sentence": null
}
source_refs
[
  "paper:paper-733c0535ffcd"
]
source_span
In sensory cortex, pyramidal neurons integrate bottom–up and top–down signals under the modulatory influence of key interneuron classes: parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP), and LAMP5-expressing neurogliaform cells each of which mediates distinct computational roles including gain control, disinhibition, and normalization respectively ( 6 – 12 ).
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": "95e761177f7d2ec565983d3307c14ec238f9677c",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewVIP"
}

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