Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Mihaljevic, Bojan and Benavides-Piccione, Ruth and Bielza Lozoya, María Concepción and De Felipe Oroquieta, Javier and Larrañaga Múgica, Pedro María (2014). Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. "Frontiers in computational neuroscience", v. 8 ; pp. 1-13. ISSN 1662-5188. https://doi.org/10.3389/fncom.2014.00150.

Description

Title: Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty
Author/s:
  • Mihaljevic, Bojan
  • Benavides-Piccione, Ruth
  • Bielza Lozoya, María Concepción
  • De Felipe Oroquieta, Javier
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: Frontiers in computational neuroscience
Date: November 2014
ISSN: 1662-5188
Volume: 8
Subjects:
Freetext Keywords: Probabilistic labels, consensus, distance-weighted k nearest neighbors, multiple annotators, neuronal morphology
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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 neuroscientists 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 neuroscientists’ 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 an 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. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2013-41592-PUnspecifiedUnspecifiedUnspecified
Madrid Regional GovernmentS2013/ICE-2845-CASI-CAM-CMUnspecifiedUnspecifiedUnspecified
FP7604102HBPEcole Polytechnique Federale de LausanneThe Human Brain Project

More information

Item ID: 35436
DC Identifier: http://oa.upm.es/35436/
OAI Identifier: oai:oa.upm.es:35436
DOI: 10.3389/fncom.2014.00150
Official URL: http://journal.frontiersin.org/article/10.3389/fncom.2014.00150/full
Deposited by: Memoria Investigacion
Deposited on: 01 Mar 2016 20:33
Last Modified: 14 May 2019 11:14
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