Details
- scope
- Mouse barrel cortex L4 (vS1), data-constrained spiking network model + in vivo recordings
- section_id
- section_09
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_09_evidence_package.json
- effect_size
- elevating recurrent excitatory conductance by ~2× beyond optimal causes runaway excitation; balanced regime gives proportional firing-rate scaling
- 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
- amplifies touch responsesand() [], mostly by increasing the duration of touch responses ().
- study_system
- Mouse barrel cortex L4 (vS1), data-constrained spiking network model + in vivo recordings
- section_title
- 9. Physiological signature I — recurrent amplification of weak inputs in mouse cortex; balanced-amplification regimes; ISN operation
- evidence_summary
- Data-constrained spiking network model of mouse L4 barrel cortex including VPM thalamic, L4 excitatory, and FS interneuron populations; parameter sweep on recurrent excitatory weight; validated against cell-type-specific in vivo recordings.
- review_bundle_ref
- analysis_bundle:ab-d9c479db9be9
- replication_status
- within_lab
- 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
In mouse barrel cortex L4, recurrent excitation among E neurons amplifies thalamic touch responses; data-constrained network model with strong synapses operates in a balanced regime where moderate recurrent E→E gain extends response duration but excessive gain causes runaway excitation.
- raw_fields
{ "n": 0, "doi": "10.1371/journal.pcbi.1005576", "claim": "In mouse barrel cortex L4, recurrent excitation among E neurons amplifies thalamic touch responses; data-constrained network model with strong synapses operates in a balanced regime where moderate recurrent E→E gain extends response duration but excessive gain causes runaway excitation.", "cite_key": "Gutnisky2017", "evidence": "Data-constrained spiking network model of mouse L4 barrel cortex including VPM thalamic, L4 excitatory, and FS interneuron populations; parameter sweep on recurrent excitatory weight; validated against cell-type-specific in vivo recordings.", "effect_size": "elevating recurrent excitatory conductance by ~2× beyond optimal causes runaway excitation; balanced regime gives proportional firing-rate scaling", "text_access": "fulltext", "study_system": "Mouse barrel cortex L4 (vS1), data-constrained spiking network model + in vivo recordings", "argument_role": "supporting", "replication_status": "within_lab", "claim_source_sentence": "amplifies touch responsesand() [], mostly by increasing the duration of touch responses ().", "source_provenance_status": "ok", "replication_evidence_dois": [], "effect_size_source_sentence": "Similarly,> 0 is required to amplify touch signals, but elevatingby a factor of two beyond the optimal level causes run-away excitation ()." }- source_refs
[ "paper:02525fad-059b-4ffc-ba1c-e5445baddd4d" ]
- evidence_refs
[ { "ref": "paper:02525fad-059b-4ffc-ba1c-e5445baddd4d" } ]- 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" }