Observatorio de I+D+i UPM

Memorias de investigación
Capítulo de libro:
Semi-supervised projected clustering for classifying GABAergic interneurons
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
A systematic classification of neuron types is a critical topic of debate in neuroscience. In this study, we propose a semi-supervised projected clustering algorithm based on finite mixture models and the expectation-maximization (EM) algorithm, that is useful for classifying neuron types. Specifically, we analyzed cortical GABAergic interneurons from different animals and cortical layers. The new algorithm, called SeSProC, is a probabilistic approach for classifying known classes and for discovering possible new groups of interneurons. Basic morphological features containing information about axonal and dendritic arborization sizes and orientations are used to characterize the interneurons. SeSProC also identifies the relevance of each feature and group separately. This article aims to present the methodological approach, reporting results for known classes and possible new groups of interneurons.
Internacional
Si
DOI
Edición del Libro
Editorial del Libro
Springer
ISBN
978-3-642-38325-0
Serie
0302-9743
Título del Libro
Lecture notes in Artificial Intelligence 7885
Desde página
156
Hasta página
165
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Luis Pelayo Guerra Velasco (UPM)
  • Autor: Ruth Benavides Piccione (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Victor Robles Forcada (UPM)
  • Autor: Javier De Felipe Oroquieta (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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