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Author Emihaljević, Bojan ♦ Edefelipe, Javier ♦ Elarrañaga, Pedro ♦ Ebenavidespiccione, Ruth ♦ Ebielza, Concha
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2015-09-11
Copyright Year ©2014
Language English
Subject Domain (in LCC) RC321-571
Subject Keyword Neuropsychiatry ♦ Consensus ♦ Biological psychiatry ♦ Probabilistic labels ♦ Neurosciences ♦ Multiple annotators ♦ Internal medicine ♦ Medicine ♦ Distance-weighted k nearest neighbors ♦ Neuronal morphology
Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neurocientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neurocientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts a LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels and that the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and therefore might serve as objective counterparts to the subjective, categorical axonal features.
ISSN 16625188
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2014-11-01
e-ISSN 16625188
Journal Frontiers in Computational Neuroscience
Volume Number 8


Source: Directory of Open Access Journals (DOAJ)