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

Mihaljevic, Bojan; Benavides-Piccione, Ruth; Bielza Lozoya, María Concepción; De Felipe Oroquieta, Javier y 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.

Descripción

Título: Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty
Autor/es:
  • Mihaljevic, Bojan
  • Benavides-Piccione, Ruth
  • Bielza Lozoya, María Concepción
  • De Felipe Oroquieta, Javier
  • Larrañaga Múgica, Pedro María
Tipo de Documento: Artículo
Título de Revista/Publicación: Frontiers in computational neuroscience
Fecha: Noviembre 2014
Volumen: 8
Materias:
Palabras Clave Informales: Probabilistic labels, consensus, distance-weighted k nearest neighbors, multiple annotators, neuronal morphology
Escuela: E.T.S.I. de Sistemas Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (1MB) | Vista Previa

Resumen

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.

Más información

ID de Registro: 35436
Identificador DC: http://oa.upm.es/35436/
Identificador OAI: oai:oa.upm.es:35436
Identificador DOI: 10.3389/fncom.2014.00150
URL Oficial: http://journal.frontiersin.org/article/10.3389/fncom.2014.00150/full
Depositado por: Memoria Investigacion
Depositado el: 01 Mar 2016 20:33
Ultima Modificación: 02 Mar 2016 20:46
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM