Segev and colleagues derived a new representation of neuronal trees
depending on electrotonic measures.
The method used for this representation was called the “Morphoelectrotonic Transform”
(*Zador et al. 1995, J Neurosci 15(3):1669-82*).

In fact, the method used in that case is very generalizable.
Any *Nx1* vector of length values may be mapped on a tree with *N* nodes.
This is done by scaling the length value *l _{i}* of all segments
to the new segment lengths while conserving the direction of the segment indicated
by the direction vector (arrow below).
At each step the entire sub-tree needs to be translated accordingly.

Only in the case of 0-length segments, a direction needs to be picked arbitrarily. A TREES toolbox function (see “morph_tree”), performs this type of morphing operation which can have various applications of which the morpho-electrotonic transform is just one.

On the path sum (see “Pvec_tree”),
child sum (see “child_tree”),
segment binning (see “bin_tree”)
parent daughter ratio (see “ratio_tree”),
are further examples of such “meta-functions” which apply an *Nx1* vector
on a tree structure to result in a wide variety of applications.

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