Details
- scope
- mouse V1 superficial layers; large-scale recurrent network model constrained by paired-recording data and compared with in vivo two-photon recordings
- claim_text
- A feature-binding excitatory subnetwork — rather than a pure like-to-like wiring rule — best reproduces in vivo facilitatory plaid responses in mouse V1, implying recurrent E→E connections combine multiple feedforward tuning channels.
- section_id
- section_05
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_05_evidence_package.json
- effect_size
- Feature-binding scheme reproduces selective amplification of plaid responses not captured by like-to-like alone
- review_repo
- ComputationalReviewRecurrence
- section_ref
- wiki_page:computationalreviewrecurrence-05-horizontal
- source_kind
- review_finding
- source_path
- evidence/section_05_evidence_package.json
- source_span
- By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity.
- study_system
- mouse V1 superficial layers; large-scale recurrent network model constrained by paired-recording data and compared with in vivo two-photon recordings
- section_title
- 5. Horizontal long-range intracortical excitatory connections in mouse — patchy L2/3-L5 axons, similarity tuning, distance-decay
- evidence_summary
- Network simulations of mouse V1 superficial layers under like-to-like vs feature-binding wiring constrained by paired-recording statistics; compared with in vivo two-photon recordings of plaid responses.
- review_bundle_ref
- analysis_bundle:ab-d9c479db9be9
- replication_status
- single_study
- review_package_ref
- analysis_bundle:ab-d9c479db9be9
- source_artifact_ref
- wiki_page:computationalreviewrecurrence-05-horizontal
- origin_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_05_evidence_package.json
- commit_sha
- 79ce062d54a924ce05953ec90aa9d26044d2b48f
- created_by
- persona-jerome-lecoq-gbo-neuroscience
- repository_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (4)
- raw_fields
{ "n": 0, "doi": "10.1371/journal.pcbi.1005888", "claim": "A feature-binding excitatory subnetwork — rather than a pure like-to-like wiring rule — best reproduces in vivo facilitatory plaid responses in mouse V1, implying recurrent E→E connections combine multiple feedforward tuning channels.", "cite_key": "Muir2017", "evidence": "Network simulations of mouse V1 superficial layers under like-to-like vs feature-binding wiring constrained by paired-recording statistics; compared with in vivo two-photon recordings of plaid responses.", "effect_size": "Feature-binding scheme reproduces selective amplification of plaid responses not captured by like-to-like alone", "text_access": "abstract_only", "study_system": "mouse V1 superficial layers; large-scale recurrent network model constrained by paired-recording data and compared with in vivo two-photon recordings", "argument_role": "supporting", "replication_status": "single_study", "claim_source_sentence": "By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity.", "source_provenance_status": "non_substring_match", "replication_evidence_dois": [], "effect_size_source_sentence": "Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1." }- source_refs
[ "paper:paper-8d890b2475da" ]
- evidence_refs
[ { "ref": "paper:paper-8d890b2475da" } ]- 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" }