Details
- scope
- SSN model relevant to cortex (cat/mouse); theoretical / numerical
- section_id
- section_09
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json
- effect_size
- qualitative — three new dynamical regimes derived analytically
- 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
- In particular, we show that the SSN model supports the following three effects.
- study_system
- SSN model relevant to cortex (cat/mouse); theoretical / numerical
- section_title
- 9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation
- evidence_summary
- Analytic and numerical bifurcation analysis of two-population SSN with strong recurrent E→E coupling stabilized by inhibition; identifies connectivity regimes yielding bistability, persistent activity, and Hopf-induced oscillations.
- 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
The stabilized supralinear network (SSN) supports bistable, oscillatory, and persistent activity regimes — extending the SSN framework beyond normalization/surround suppression to dynamics relevant for working memory and decision making in cortex.
- raw_fields
{ "n": 0, "doi": "10.1073/pnas.1700080115", "claim": "The stabilized supralinear network (SSN) supports bistable, oscillatory, and persistent activity regimes — extending the SSN framework beyond normalization/surround suppression to dynamics relevant for working memory and decision making in cortex.", "cite_key": "Kraynyukova2018", "evidence": "Analytic and numerical bifurcation analysis of two-population SSN with strong recurrent E→E coupling stabilized by inhibition; identifies connectivity regimes yielding bistability, persistent activity, and Hopf-induced oscillations.", "effect_size": "qualitative — three new dynamical regimes derived analytically", "text_access": "fulltext", "study_system": "SSN model relevant to cortex (cat/mouse); theoretical / numerical", "argument_role": "supporting", "replication_status": "independently_replicated", "claim_source_sentence": "In particular, we show that the SSN model supports the following three effects.", "source_provenance_status": "ok", "replication_evidence_dois": [ "10.7554/eLife.54875", "10.1523/JNEUROSCI.2830-20.2021" ], "effect_size_source_sentence": null }- source_refs
[ "paper:paper-5989bc007d71" ]
- evidence_refs
[ { "ref": "paper:paper-5989bc007d71" } ]- 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" }