50-Cent/ElasticPath2Path

Morphological categorization of neurons in order to explore their functional features has drawn significant attention over past few decades. The enormous complexity in the structure of neurons poses a real challenge in the identification and analysis of similar and dissimilar neuronal cells. Existing methodologies often carry out strutural and geometrical simplifications, which substantially changes the morphological statistics. Using digitally-reconstructed neurons, we extend the work of Path2Path as ElasticP2P, which seamlessly integrates the graph-theoretic and differential-geometric frameworks. By decomposing a neuron into a set of paths, we derive graph metrics, which are path concurrence and path hierarchy. Next, we model each path as an elastic string to compute the geodesic distance between the paths of a pair of neurons. Later, we formulate the problem of finding the distance between two neurons as a path assignment problem with a cost function combining the graph metrics and the geodesic deformation of paths.

Language: Matlab - Size: 19.5 KB - Last synced at: over 1 year ago - Pushed at: about 7 years ago - Stars: 1 - Forks: 0