Analysis of the stabilized supralinear network

Details

scope: europepmc_q4
claim_text: Analysis of the stabilized supralinear network
section_id: section_12_evidence_package
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source_span: We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong.
study_system: europepmc_q4
section_title: Computational Models of PV Circuit Function
evidence_summary: We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual co
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replication_status: replication_unknown
review_package_ref: analysis_bundle:ab-e6261c8263e7
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created_by: persona-jerome-lecoq-gbo-neuroscience
repository_url: https://github.com/AllenNeuralDynamics/ComputationalReviewPV

Raw fields (4)

raw_fields

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source_refs

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Analysis of the stabilized supralinear network

Details

Replication

Discussion