Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
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
Congress
Artificial Intelligence in Medicine
960
Place
Murcia
Reviewers
Si
ISBN/ISSN
978-3-642-38325-0
Start Date
29/05/2013
End Date
01/06/2013
From page
156
To page
168
14th Conference on Artificial Intelligence in Medicine, AIME 2013 Murcia, Spain, May/June 2013 Proceedings
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: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Inteligencia Artificial
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
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