  Mapping the connectivity of neurons in the brain (i.e., connectomics) is a
challenging problem due to both the number of connections in even the smallest
organisms and the nanometer resolution required to resolve them. Because of
this, previous connectomes contain only hundreds of neurons, such as in the
C.elegans connectome. Recent technological advances will unlock the mysteries
of increasingly large connectomes (or partial connectomes). However, the value
of these maps is limited by our ability to reason with this data and understand
any underlying motifs. To aid connectome analysis, we introduce algorithms to
cluster similarly-shaped neurons, where 3D neuronal shapes are represented as
skeletons. In particular, we propose a novel location-sensitive clustering
algorithm. We show clustering results on neurons reconstructed from the
Drosophila medulla that show high-accuracy.
