Texto completo
Vista Previa |
PDF (Portable Document Format)
- Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (1MB) | Vista Previa |
Mihaljevic, Bojan ORCID: https://orcid.org/0000-0002-1656-6135, Benavides-Piccione, Ruth, Bielza Lozoya, María Concepción ORCID: https://orcid.org/0000-0001-7109-2668, De Felipe Oroquieta, Javier and Larrañaga Múgica, Pedro María ORCID: https://orcid.org/0000-0003-0652-9872 (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.
Título: | Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty |
---|---|
Autor/es: |
|
Tipo de Documento: | Artículo |
Título de Revista/Publicación: | Frontiers in computational neuroscience |
Fecha: | Noviembre 2014 |
ISSN: | 1662-5188 |
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 |
Vista Previa |
PDF (Portable Document Format)
- Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (1MB) | Vista Previa |
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.
ID de Registro: | 35436 |
---|---|
Identificador DC: | https://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/fnc... |
Depositado por: | Memoria Investigacion |
Depositado el: | 01 Mar 2016 20:33 |
Ultima Modificación: | 20 Mar 2024 18:37 |