Bayesian network classifiers for categorizing cortical gABAergic interneurons

Mihaljevic, Bojan; Bielza Lozoya, María Concepción; Larrañaga Múgica, Pedro María; Benavides Piccione, Ruth y Felipe Oroquieta, Javier de (2015). Bayesian network classifiers for categorizing cortical gABAergic interneurons. "Neuroinformatics", v. 13 (n. 2); pp. 193-208. ISSN 1539-2791.


Título: Bayesian network classifiers for categorizing cortical gABAergic interneurons
  • Mihaljevic, Bojan
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
  • Benavides Piccione, Ruth
  • Felipe Oroquieta, Javier de
Tipo de Documento: Artículo
Título de Revista/Publicación: Neuroinformatics
Fecha: Abril 2015
Volumen: 13
Palabras Clave Informales: Keywords : Neuronal classification · Morphological features · Label reliability · Multiple annotators · Weightednaive Bayes
Escuela: E.T.S.I. de Sistemas Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Abstract An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1–F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts’ assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell’s label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1–F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52 % accuracy, and single out the number of branches at 180 µm from the soma, the convex hull 2D area, and axonal features F1–F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

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ID de Registro: 41018
Identificador DC:
Identificador OAI:
Identificador DOI: 10.1007/s12021-014-9254-1
URL Oficial:
Depositado por: Memoria Investigacion
Depositado el: 27 Abr 2017 18:03
Ultima Modificación: 27 Abr 2017 21:53
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