Details
- scope
- mouse visual cortex, deep learning foundation model, MICrONS connectomics
- claim_text
- A foundation model of neural activity trained on mouse visual cortex data can predict neuronal connectivity within the MICrONS functional connectomics dataset.
- section_id
- section_05
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_05_evidence_package.json
- effect_size
- review_repo
- ComputationalReviewRecurrence
- section_ref
- wiki_page:computationalreviewrecurrence-05-horizontal
- source_kind
- review_finding
- source_path
- evidence/section_05_evidence_package.json
- source_span
- Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset.
- study_system
- mouse visual cortex, deep learning foundation model, MICrONS connectomics
- section_title
- 5. Horizontal long-range intracortical excitatory connections in mouse — patchy L2/3-L5 axons, similarity tuning, distance-decay
- evidence_summary
- A deep learning foundation model was trained on large-scale neural activity recordings from visual cortices of multiple mice and evaluated on predicting connectivity in the MICrONS dataset.
- review_bundle_ref
- analysis_bundle:ab-d9c479db9be9
- replication_status
- novel
- 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": "10.1038/s41586-025-08829-y", "claim": "A foundation model of neural activity trained on mouse visual cortex data can predict neuronal connectivity within the MICrONS functional connectomics dataset.", "cite_key": "Wang2025b", "evidence": "A deep learning foundation model was trained on large-scale neural activity recordings from visual cortices of multiple mice and evaluated on predicting connectivity in the MICrONS dataset.", "effect_size": "", "text_access": "abstract_only", "study_system": "mouse visual cortex, deep learning foundation model, MICrONS connectomics", "argument_role": "supporting", "replication_status": "novel", "claim_source_sentence": "Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset.", "source_provenance_status": "non_substring_match", "replication_evidence_dois": [], "effect_size_source_sentence": "" }- source_refs
[ "paper:paper-a802f6ac77d4" ]
- evidence_refs
[ { "ref": "paper:paper-a802f6ac77d4" } ]- 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" }