Interrelating neuronal morphology by coincidence similarity networks

Abstract

The study of neuronal morphology presents potential not only for identifying possible relationship with neuronal dynamics, but also as a means to characterize and classify types of neuronal cells and compare them among species, organs, and conditions. In the present work, we approach this problem by using the concept of coincidence similarity index, considering a methodology for mapping datasets into similarity networks. The adopted similarity presents some specific interesting properties, including more strict comparisons. A set of 20 morphological features has been considered, and coincidence similarity networks estimated respectively to 735 considered neuronal cells from 8 groups of Drosophila melanogaster.