Browse by author
Lookup NU author(s): Professor Marcus Kaiser
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the NeuroMorpho.org database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.
Author(s): Zawadzki K, Feenders C, Viana MP, Kaiser M, Costa LD
Publication type: Article
Publication status: Published
Print publication date: 22/05/2012
ISSN (print): 1539-2791
ISSN (electronic): 1559-0089
Altmetrics provided by Altmetric