Details
- 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.
- 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 E/I assembly learning model
- section_title
- Computational Models of VIP Circuit Function
- 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
Raw fields (5)
- 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 }- 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.
- evidence_refs
[ { "ref": "paper:paper-ce8a09f59a46" } ]- 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" }