![]() ![]() We restricted radius scaling ratio data to values that are less than 1.0. The closest theoretical predictions for the axon scaling ratio mean are the optimal scaling ratios for function T, minimizing time delay, the myelinated case with ϵ = 1 2, n - 1 / 3 ≈ 0.79, and the unmyelinated case with ϵ = 0, n - 2 / 5 ≈ 0.76. The closest theoretical predictions for the dendrite scaling ratio mean are the optimal scaling ratios for function P, minimizing power with fixed volume, n - 1 / 2 ≈ 0.71, and for function P ∗, minimizing power with fixed time delay, n - 2 / 3 ≈ 0.63. The black solid lines denote the mean in the distributions, shown with error bars, and the red, green, blue, and magenta dashed lines represent the theoretical predictions for various objective functions. The standard deviations of the distributions are 0.20 for dendrites and 0.17 for axons. In the figure, μ represents the mean and SEM represents the standard error of the mean (SEM). The mean dendrite scaling ratio is 0.67 ± 0.004 and the mean axon scaling ratio is 0.79 ± 0.01. Histograms showing the distributions of radius scaling ratios for axons and dendrites combined from a range of species, brain regions, and cell types available on NeuroMorpho.Org. Government work and not under copyright protection in the US foreign copyright protection may apply.Ĭomparison of dendrite and axon radius scaling ratio distributions, combined. Our model also predicts a quarter-power scaling relationship between conduction time delay and body size. Notably, our findings reveal that the branching of axons and peripheral nervous system neurons is mainly determined by time minimization, while dendritic branching is determined by power minimization. We test our predictions for radius scale factors against those extracted from neuronal images, measured for species that range from insects to whales, including data from light and electron microscopy studies. Here, by constructing biophysical theory and testing against empirical measures of branching structure, we develop a general model that establishes a correspondence between neuron structure and function as mediated by principles such as time or power minimization for information processing as well as spatial constraints for forming connections. Classifying neurons according to differences in structure or function is a fundamental part of neuroscience. Neurons are connected by complex branching processes-axons and dendrites-that process information for organisms to respond to their environment. ![]()
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