ArticleNature communications2025
An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Pronounced Neuroplasticity in the Primary Visual Cortex of the 13-Lined Ground Squirrel during Hibernation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Cell-type-specific parallel pathways in the canonical cortical microcircuit.bioRxiv : the preprint server for biology · 2026Article
- Synaptic-resolution connectomics: towards large brains and connectomic screening.Nature reviews. Neuroscience · 2026Review
- Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.Brain informatics · 2026Article
- Diversity in Whisking-Related Dynamics of Layer 5 Neurons in the Motor Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- The grand challenge in cellular neurophysiology.Frontiers in cellular neuroscience · 2026Article
- Deep learning models to map osteocyte networks from confocal microscopy can successfully distinguish between young and aged bone.PLoS computational biology · 2026Article
- VENturing into machine learning for the morphological analysis of von Economo neurons.Scientific reports · 2025Article
- Learning to cluster neuronal function.ArXiv · 2025Article
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- Optimal interphase delay in biphasic current pulses facilitates neural circuit activation induced by microstimulation in the mouse visual cortex.Frontiers in neuroscience · 2025Article
- Functional connectomics reveals general wiring rule in mouse visual cortex.bioRxiv : the preprint server for biology · 2024Article
- Learning meaningful representation of single-neuron morphology via large-scale pre-training.Bioinformatics (Oxford, England) · 2024Article
- Foundation model of neural activity predicts response to new stimulus types and anatomy.bioRxiv : the preprint server for biology · 2024Article
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48 authors.
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Abstract
Neurons in the neocortex exhibit astonishing morphological diversity, which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL, and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5, which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons' morphological diversity is better understood by considering axes of variation than using distinct m-types.
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