- raw_fields
{
"n": 0,
"doi": "10.1016/j.neuron.2014.12.026",
"claim": "The stabilized supralinear network (SSN) is a unifying circuit motif underlying multi-input integration: surround suppression, normalization, contrast invariance emerge from supralinear I/O, recurrent excitation, and feedback inhibition",
"evidence": "Theoretical analysis and new recordings in visual cortex confirming model predictions",
"effect_size": null,
"text_access": "fulltext",
"study_system": "SSN circuit model, mouse visual cortex recordings",
"replication_status": "replication_unknown",
"claim_source_sentence": "A wealth of integrative properties including the above emerge robustly from four properties of cortical circuitry: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; (4) simple spatial properties of intracortical connections.",
"replication_evidence_dois": [],
"effect_size_source_sentence": null
}- source_refs
[
"paper:paper-e15053dfa039"
]
- source_span
A wealth of integrative properties including the above emerge robustly from four properties of cortical circuitry: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; (4) simple spatial properties of intracortical connections.
- evidence_refs
[
{
"ref": "paper:paper-e15053dfa039"
}
]- 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": "df9fc7e8d455b084152c9d713558dae0013cef21",
"source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewPV"
}