Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011).
This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.
* L. d. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser. Beyond the average: Detecting global singular nodes from local features in complex networks, Europhysics Letters, 87, 1 (2009)
* C. Echtermeyer, L. d. Fontoura Costa, F. A. Rodrigues, M. Kaiser. Automatic Network Fingerprinting Through Single-Node Motifs, PLoS ONE 6, 9 (2011)
* K. d. Zawadzki, C. Feenders, M. P. Viana, M. Kaiser, and L. d. Fontoura Costa. Morphological Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells, Neuroinformatics (2012)
Resource Type: Resource
Version: Latest Version
neuron, feature-space, archetype, outlier, matlab, neuromorphometry, computational neuroscience
FAPESP, CNPq, EPSRC, Code Analysis Repository and Modelling for e-Neuroscience, Korean Ministry of Education Science and Technology, 05/00587-5, 301303/06-1, 2010/01994-1, 2010/16310-0, 573583/2008-0, EP/G03950X/1, R32-10142
Additional Resource Types
GNU General Public License
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Detection of Outlier NEurons
Created 3 years ago by Anonymous