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. 2018 Oct 23;8:15666. doi: 10.1038/s41598-018-34050-1

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison between actual neuron data and model. Top two panels show relative difference on quantities including tree size N, percentage of B-type branching nodes B(%), tree topological length k_max and width j_max as well as tree asymmetry A from (in)homogeneous modelling to those measured from actual axonal (left) and dendritic (right) trees respectively. Bottom panels show distributions on tree size N and topological shape parameters (k_max and j_max). Two-sample Kolmogorov-Smirnov test is used to compare between distributions from actual axonal (left) and dendritic (right) trees and virtual trees generated using the (in)homogeneous model; p values are indicated for each comparison. The imhomogeneous model uses the best fitted exponential decay with non-zero plateau of the branching probability given in Fig. 6.