Observatorio de I+D+i UPM

Memorias de investigación
Book chapters:
Semi-supervised projected clustering for classifying GABAergic interneurons
Year:2013
Research Areas
  • Artificial intelligence
Information
Abstract
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.
International
Si
Book Edition
Book Publishing
Springer
ISBN
978-3-642-38325-0
Series
0302-9743
Book title
Lecture notes in Artificial Intelligence 7885
From page
156
To page
165
Participants
  • 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)
Research Group, Departaments and Institutes related
  • Creador: Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
  • Departamento: Inteligencia Artificial
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