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Descripción
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| 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. | |
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Internacional
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Si |
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Nombre congreso
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Artificial Intelligence in Medicine |
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Tipo de participación
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960 |
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Lugar del congreso
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Murcia |
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Revisores
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Si |
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ISBN o ISSN
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978-3-642-38325-0 |
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DOI
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Fecha inicio congreso
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29/05/2013 |
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Fecha fin congreso
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01/06/2013 |
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Desde la página
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156 |
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Hasta la página
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168 |
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Título de las actas
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14th Conference on Artificial Intelligence in Medicine, AIME 2013 Murcia, Spain, May/June 2013 Proceedings |