 Functional connectomics reveals general wiring rule in mouse visual cortexZhuokun Ding,
Paul G. Fahey,
Stelios Papadopoulos,
Eric Y. Wang,
Brendan Celii,
Christos Papadopoulos,
Andersen Chang,
Alexander B. Kunin,
Dat Tran,
Jiakun Fu,
Zhiwei Ding,
Saumil Patel,
Lydia Ntanavara,
Rachel Froebe,
Kayla Ponder,
Taliah Muhammad,
J. Alexander Bae,
Agnes L. Bodor,
Derrick Brittain,
JoAnn Buchanan,
Daniel J. Bumbarger,
Manuel A. Castro,
Erick Cobos,
Sven Dorkenwald,
Leila Elabbady,
Akhilesh Halageri,
Zhen Jia,
Chris Jordan,
Dan Kapner,
Nico Kemnitz,
Sam Kinn,
Kisuk Lee,
Kai Li,
Ran Lu,
Thomas Macrina,
Gayathri Mahalingam,
Eric Mitchell,
Shanka Subhra Mondal,
Shang Mu,
Barak Nehoran,
Sergiy Popovych,
Casey M. Schneider-Mizell,
William Silversmith,
Marc Takeno,
Russel Torres,
Nicholas L. Turner,
William Wong,
Jingpeng Wu,
Wenjing Yin,
Szi-chieh Yu,
Dimitri Yatsenko,
Emmanouil Froudarakis,
Fabian Sinz,
Krešimir Josić,
Robert Rosenbaum,
H. Sebastian Seung,
Forrest Collman,
Nuno Maçarico Costa,
R. Clay Reid,
Edgar Y. Walker,
Xaq Pitkow,
Jacob Reimer () and
Andreas S. Tolias ()
Nature, 2025, vol. 640, issue 8058, 459-469 Abstract: 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. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:640:y:2025:i:8058:d:10.1038_s41586-025-08840-3 Ordering information: This journal article can be ordered from DOI: 10.1038/s41586-025-08840-3 Access Statistics for this article Nature is currently edited by Magdalena Skipper More articles in Nature from Nature |
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