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

Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla