Functional connectomics reveals general wiring rule in mouse visual cortex

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 connected<sup>1-8</sup>; 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.

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Publication Details

Journal
Nature
Published
2025-04-09
DOI
https://doi.org/10.1038/s41586-025-08840-3
Citations
38
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
52.95
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article

Functional connectomics reveals general wiring rule in mouse visual cortex

Ágnes L. Bodor, R. Clay Reid, Dat Tran, Alexander B. Kunin et al.
38 citations
Nature
Neural dynamics and brain function
52.95
article

Functional connectomics reveals general wiring rule in mouse visual cortex

Ágnes L. Bodor, R. Clay Reid, Dat Tran, Alexander B. Kunin, Manuel Castro, Sergiy Popovych, JoAnn Buchanan, Szi-chieh Yu, Stelios Papadopoulos, Taliah Muhammad, Nuno Maçarico da Costa, Krešimir Josić́, Thomas Macrina, Paul G. Fahey, Russel Torres, Sven Dorkenwald, Shanka Subhra Mondal, Emmanouil Froudarakis, Leila Elabbady, Zhiwei Ding, Robert Rosenbaum, Fabian H. Sinz, Zhuokun Ding, Nicholas L. Turner, H. Sebastian Seung, William Silversmith, Jiakun Fu, Nico Kemnitz, Jingpeng Wu, Edgar Y. Walker, Sam Kinn, Marc Takeno, Daniel J. Bumbarger, Andreas S. Tolias, Shang Mu, Xaq Pitkow, Akhilesh Halageri, Brendan Celii, Kayla Ponder, Jacob Reimer, Andersen Chang, Casey M Schneider-Mizell, Eric Mitchell, Gayathri Mahalingam, Dan Kapner, Forrest Collman, Barak Nehoran, Erick Cobos, Derrick Brittain, Saumil S. Patel, Rachel Froebe, Kai Li, Eric Wang, J. Alexander Bae, Christos Papadopoulos, Wenjing Yin, Kisuk Lee, Lydia Ntanavara, Zhen Jia, Dimitri Yatsenko, William Wong, Ran Lu, Chris Jordan
article en
38 citations

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 connected<sup>1-8</sup>; 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.

NatureVol. 640(8058)
University of Notre Dame (US), Creighton University (US), Salk Institute for Biological Studies (US), Allen Institute for Brain Science (US), University of Crete (GR), Baylor College of Medicine (US), Neurosciences Institute (US), Princeton University (US), University of Washington (US), DataJoint NEURO (United States) (US), Institute for Small Business Economics (DE), Stanford Medicine (US), Allen Institute (US), University of Houston (US), Massachusetts Institute of Technology (US), University of Göttingen (DE), Rice University (US), Carnegie Mellon University (US), Foundation for Research and Technology Hellas (GR), Stanford University (US)
Openalex Percentile: Top 0%
Neural dynamics and brain function
52.95
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