ArticleNetwork neuroscience (Cambridge, Mass.)2025
Combined topological and spatial constraints are required to capture the structure of neural connectomes.
Article in Network neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Physical contact reveals a hidden layer of cortical architecture.bioRxiv : the preprint server for biology · 2026Article
- Variation in high-amplitude events across the human lifespan.Network neuroscience (Cambridge, Mass.) · 2026Article
- Learning and criticality in a self-organizing model of connectome growth.Scientific reports · 2025Article
- Short-range human cortico-cortical white matter fibers have thinner axons and are less myelinated compared to long-range fibers despite a similar g-ratio.PLoS biology · 2025Article
- Effective methods and framework for energy-based local learning of deep neural networks.Frontiers in artificial intelligence · 2025Article
- Network dismantling by physical damage.Communications physics · 2025Article
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2 authors.
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Abstract
Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints-either given by pairwise distances or the contactome-play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. Yet, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent mismatch by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics.
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