Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla
This aids connectome analysis for neuroscience by enabling better reasoning with neuronal data, though it appears incremental as it builds on existing shape representation methods.
The authors tackled the problem of analyzing large connectomes by introducing algorithms to cluster similarly-shaped neurons using 3D skeletons, achieving high-accuracy results on Drosophila medulla neurons.
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.