Version history

{
  "scope": "spiking E/I assembly learning model",
  "claim_text": "Recurrent spiking network with biologically realistic PV, SST and VIP populations learns excitatory-inhibitory neuronal assemblies; VIP disinhibition gates assembly recruitment.",
  "raw_fields": {
    "n": null,
    "id": "cluster_11_finding_63",
    "doi": "10.7554/elife.59715",
    "pmid": "33900199",
    "year": "2021",
    "claim": "Recurrent spiking network with biologically realistic PV, SST and VIP populations learns excitatory-inhibitory neuronal assemblies; VIP disinhibition gates assembly recruitment.",
    "pmcid": "PMC8075581",
    "title": "Learning excitatory-inhibitory neuronal assemblies in recurrent networks.",
    "authors": "Mackwood O, Naumann LB, Sprekeler H.",
    "journal": "eLife",
    "cite_key": "Mackwood2021",
    "evidence": "Spiking network with STDP-like rules across multiple interneuron types; quantifies how VIP gates lateral inhibition during learning.",
    "effect_size": null,
    "text_access": "fulltext",
    "study_system": "spiking E/I assembly learning model",
    "_source_cluster": "cluster_11_computational_models",
    "replication_status": "replicated_independent",
    "_source_cluster_index": 62,
    "claim_source_sentence": "To investigate the effect of stimulus-specific inhibition in our network, we simulate the perturbation experiment of Chettih and Harvey, 2019 : First, we again expose the network to the stimulus set, with PV input and output plasticity in place to learn E/I assemblies.",
    "replication_evidence_dois": [
      "10.1016/j.neuron.2023.11.006",
      "10.1101/2025.05.13.653877",
      "10.1101/2024.09.30.615819",
      "10.1016/j.neuron.2018.03.037",
      "10.1162/netn_a_00427"
    ],
    "effect_size_source_sentence": null
  },
  "section_id": "section_12",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewVIP/blob/95e761177f7d2ec565983d3307c14ec238f9677c/evidence/section_12_evidence_package.json",
  "effect_size": null,
  "review_repo": "ComputationalReviewVIP",
  "section_ref": "wiki_page:computationalreviewvip-12-computational-models",
  "source_kind": "review_finding",
  "source_path": "evidence/section_12_evidence_package.json",
  "source_refs": [
    "paper:paper-ce8a09f59a46"
  ],
  "source_span": "To investigate the effect of stimulus-specific inhibition in our network, we simulate the perturbation experiment of Chettih and Harvey, 2019 : First, we again expose the network to the stimulus set, with PV input and output plasticity in place to learn E/I assemblies.",
  "study_system": "spiking E/I assembly learning model",
  "evidence_refs": [
    {
      "ref": "paper:paper-ce8a09f59a46"
    }
  ],
  "section_title": "Computational Models of VIP Circuit Function",
  "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": "Spiking network with STDP-like rules across multiple interneuron types; quantifies how VIP gates lateral inhibition during learning.",
  "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"
}