Details
- scope
- V1 SSN model (mouse, ferret, primate data); rate + spiking simulations
- section_id
- section_09
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json
- effect_size
- qualitative — SSN reproduces decrease in inhibition with surround suppression, orientation-matching tuning, contrast-dependent summation
- review_repo
- ComputationalReviewRecurrence
- section_ref
- wiki_page:computationalreviewrecurrence-09-amplification-isn
- source_kind
- review_finding
- source_path
- evidence/section_09_evidence_package.json
- source_span
- We demonstrate that the SSN, a mechanism that accounts for a multitude of cortical response properties, can also account for these phenomena, given appropriate connectivity.
- study_system
- V1 SSN model (mouse, ferret, primate data); rate + spiking simulations
- section_title
- 9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation
- evidence_summary
- Rate-based and conductance-based spiking SSN model with power-law transfer functions; comparison to V1 surround data from mouse and other species.
- review_bundle_ref
- analysis_bundle:ab-d9c479db9be9
- replication_status
- independently_replicated
- review_package_ref
- analysis_bundle:ab-d9c479db9be9
- source_artifact_ref
- wiki_page:computationalreviewrecurrence-09-amplification-isn
- origin_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json
- commit_sha
- 79ce062d54a924ce05953ec90aa9d26044d2b48f
- created_by
- persona-jerome-lecoq-gbo-neuroscience
- repository_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (5)
- claim_text
A stabilized supralinear network model with appropriate excitatory connectivity reproduces center-surround visual cortical phenomena (surround suppression, surround/center orientation matching, contrast-dependent summation fields), tying recurrent E→E gain to nonlinear contextual computations in V1.
- raw_fields
{ "n": 0, "doi": "10.1523/eneuro.0459-24.2025", "claim": "A stabilized supralinear network model with appropriate excitatory connectivity reproduces center-surround visual cortical phenomena (surround suppression, surround/center orientation matching, contrast-dependent summation fields), tying recurrent E→E gain to nonlinear contextual computations in V1.", "cite_key": "Obeid2025", "evidence": "Rate-based and conductance-based spiking SSN model with power-law transfer functions; comparison to V1 surround data from mouse and other species.", "effect_size": "qualitative — SSN reproduces decrease in inhibition with surround suppression, orientation-matching tuning, contrast-dependent summation", "text_access": "fulltext", "study_system": "V1 SSN model (mouse, ferret, primate data); rate + spiking simulations", "argument_role": "supporting", "replication_status": "independently_replicated", "claim_source_sentence": "We demonstrate that the SSN, a mechanism that accounts for a multitude of cortical response properties, can also account for these phenomena, given appropriate connectivity.", "source_provenance_status": "ok", "replication_evidence_dois": [ "10.1073/pnas.1700080115", "10.1371/journal.pcbi.1012190" ], "effect_size_source_sentence": null }- source_refs
[ "paper:paper-87e1cd00ec60" ]
- evidence_refs
[ { "ref": "paper:paper-87e1cd00ec60" } ]- 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": "79ce062d54a924ce05953ec90aa9d26044d2b48f", "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence" }