Nervous systems are characterized by neurons displaying a diversity of morphological shapes. Traditionally, different shapes have been qualitatively described based on visual inspection and quantitatively described based on morphometric parameters. Neither process provides a solid foundation for categorizing the various morphologies, a problem that is important in many fields. We propose a stable topological measure as a standardized descriptor for any tree-like morphology, which encodes its skeletal branching anatomy. More specifically it is a barcode of the branching tree as determined by a spherical filtration centered at the root or neuronal soma. This Topological Morphology Descriptor (TMD) allows for the discrimination of groups of random and neuronal trees at linear computational cost.
@article{arxiv.1603.08432,
title = {Quantifying topological invariants of neuronal morphologies},
author = {Lida Kanari and Paweł Dłotko and Martina Scolamiero and Ran Levi and Julian Shillcock and Kathryn Hess and Henry Markram},
journal= {arXiv preprint arXiv:1603.08432},
year = {2016}
}
Comments
10 pages, 5 figures, conference or other essential info