  The study of neuronal morphology is important not only for its potential
relationship with neuronal dynamics, but also as a means to classify diverse
types of cells and compare than among species, organs, and conditions. In the
present work, we approach this interesting problem by using the concept of
coincidence similarity, as well as a respectively derived method for mapping
datasets into networks. The coincidence similarity has been found to allow some
specific interesting properties which have allowed enhanced performance
(selectivity and sensitivity) concerning several pattern recognition tasks.
Several combinations of 20 morphological features were considered, and the
respective networks were obtained by maximizing the literal modularity (in
supervised manner) respectively to the involved parameters. Well-separated
groups were obtained that provide a rich representation of the main similarity
interrelationships between the 735 considered neuronal cells. A sequence of
network configurations illustrating the progressive merging between cells and
groups was also obtained by varying one of the coincidence parameters.
