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. Author manuscript; available in PMC: 2022 Feb 17.
Published in final edited form as: Neuroinformatics. 2011 Mar 1;9(1):91–6. doi: 10.1007/s12021-010-9093-7

Fig. 1. Basic handling of a dendritic tree using the TREES toolbox.

Fig. 1

a A tree is represented as a structure with: a directed adjacency matrix dA attributing a parent to each node in the tree; a set of vertical vectors, such as X, Y and Z coordinates and diameter values D, each assigning a value to every node in the tree; an array rnames containing sub-region names; arbitrary optional quantities such as the global electrotonic properties Ri, Gm and Cm, specific axial resistance, membrane conductance and membrane capacitance respectively. b Most functions in the TREES toolbox take a tree as their first argument. This is the case for example for the function plot_tree, which allows a 3D interactive visualization of the sample tree loaded in (a). c Statistics and tree edit functions are generally simple and easy to use. Here the branch order (BO) values of the tree are obtained using BO_tree and all nodes with BO larger than 5 are deleted using delete_tree. The remaining tree is visualized and BO values are mapped in pseudo-colour onto the tree using the optional second argument of the function plot_tree. Axis labels were added for clarity