Functional connectomics reveals general wiring rule in mouse visual cortex.

TitleFunctional connectomics reveals general wiring rule in mouse visual cortex.
Publication TypeJournal Article
Year of Publication2025
AuthorsDing Z, Fahey PG, Papadopoulos S, Wang EY, Celii B, Papadopoulos C, Chang A, Kunin AB, Tran D, Fu J, Ding Z, Patel S, Ntanavara L, Froebe R, Ponder K, Muhammad T, J Bae A, Bodor AL, Brittain D, Buchanan JA, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SSubhra, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu S-C, Yatsenko D, Froudarakis E, Sinz F, Josić K, Rosenbaum R, H Seung S, Collman F, da Costa NMaçarico, R Reid C, Walker EY, Pitkow X, Reimer J, Tolias AS
JournalNature
Volume640
Issue8058
Pagination459-469
Date Published2025 Apr
ISSN1476-4687
KeywordsAnimals, Axons, Connectome, Dendrites, Female, Male, Mice, Models, Neurological, Neural Networks, Computer, Neurons, Primary Visual Cortex, Synapses, Visual Cortex
Abstract

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected1-8; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas-including feedback connections-supporting the universality of 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Recurrent neural networks trained on a simple classification task develop connectivity patterns that mirror both pairwise and higher-order rules, with magnitudes similar to those in MICrONS data. Ablation studies in these recurrent neural networks reveal that disrupting like-to-like connections impairs performance more than disrupting random connections. These findings suggest that these connectivity principles may have a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

DOI10.1038/s41586-025-08840-3
Alternate JournalNature
PubMed ID40205211
PubMed Central IDPMC11981947
Grant ListRF1 MH130416 / MH / NIMH NIH HHS / United States
P30 EY002520 / EY / NEI NIH HHS / United States
T15 LM007093 / LM / NLM NIH HHS / United States
U19 MH114830 / MH / NIMH NIH HHS / United States
R01 EY026927 / EY / NEI NIH HHS / United States