AI Article Synopsis
- Branching enables neurons to connect with many other neurons, crucial for the complex networks in nervous systems.
- The geometric features of neuronal shapes, like branch lengths and diameters, are designed to enhance signaling efficiency while minimizing the resources needed for development.
- Our findings indicate that neuron structures exhibit scale-invariant characteristics, with variations seen across different types of cells, and that the way branches grow impacts their functional performance.
Article Abstract
Branching allows neurons to make synaptic contacts with large numbers of other neurons, facilitating the high connectivity of nervous systems. Neuronal arbors have geometric properties such as branch lengths and diameters that are optimal in that they maximize signaling speeds while minimizing construction costs. In this work, we asked whether neuronal arbors have topological properties that may also optimize their growth or function. We discovered that for a wide range of invertebrate and vertebrate neurons the distributions of their subtree sizes follow power laws, implying that they are scale invariant. The power-law exponent distinguishes different neuronal cell types. Postsynaptic spines and branchlets perturb scale invariance. Through simulations, we show that the subtree-size distribution depends on the symmetry of the branching rules governing arbor growth and that optimal morphologies are scale invariant. Thus, the subtree-size distribution is a topological property that recapitulates the functional morphology of dendrites.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10756852 | PMC |
| http://dx.doi.org/10.1016/j.celrep.2023.113268 | DOI Listing |
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