Semi-supervised Projected Clustering for Classifying GABAergic Interneurons

  • Luis Guerra
  • Ruth Benavides-Piccione
  • Concha Bielza
  • Víctor Robles
  • Javier DeFelipe
  • Pedro Larrañaga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)


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.


Clustering semi-supervised finite mixture model EM projected cortical interneurons 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luis Guerra
    • 1
  • Ruth Benavides-Piccione
    • 2
    • 3
  • Concha Bielza
    • 1
  • Víctor Robles
    • 4
  • Javier DeFelipe
    • 2
    • 3
  • Pedro Larrañaga
    • 1
  1. 1.Computational Intelligence Group, Departamento de Inteligencia ArtificialUniversidad Politécnica de Madrid (UPM)Boadilla del MonteSpain
  2. 2.Laboratorio Cajal de Circuitos, Centro de Tecnología BiomédicaUPMSpain
  3. 3.Instituto Cajal, CSICSpain
  4. 4.Departamento de Arquitectura y Tecnología de Sistemas InformáticosUPMBoadilla del MonteSpain

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