Bayesian Gaussian networks for multidimensional classification of morphologically characterized neurons in the NeuroMorpho repository

Fernández González, Pablo and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2016). Bayesian Gaussian networks for multidimensional classification of morphologically characterized neurons in the NeuroMorpho repository. In: "XVII Conferencia de la Asociación Española para la Inteligencia Artificial", 14-16 Sep 2016, Salamanca, España. ISBN 978-84-9012-63. pp. 39-48.

Description

Title: Bayesian Gaussian networks for multidimensional classification of morphologically characterized neurons in the NeuroMorpho repository
Author/s:
  • Fernández González, Pablo
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Presentation at Congress or Conference (Article)
Event Title: XVII Conferencia de la Asociación Española para la Inteligencia Artificial
Event Dates: 14-16 Sep 2016
Event Location: Salamanca, España
Title of Book: Actas de la XVII Conferencia de la Asociación Española para la Inteligencia Artificial
Date: 2016
ISBN: 978-84-9012-63
Volume: 1
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

A class-bridge decomposable multidimensional Gaussian net- work is presented as an interpretable and powerful model, to account for the morphological di erences that exist between di erent neurons when varying the species, gender, brain region, cell types and developmental stage of the animal of origin. Also this work includes a learning algorithm that makes use of the CB-decomposablility property to alleviate the inference complexity and use it to learn complex network structures that take into account relationships between classes. The model is trained with data from NeuroMorpho (v5.7) and the nal model is used to test the predictive power of the learning algorithm for Bayesian networks and, given its interpretability, to extract knowledge at a neuroscience lev

More information

Item ID: 46646
DC Identifier: http://oa.upm.es/46646/
OAI Identifier: oai:oa.upm.es:46646
Official URL: http://congresocedi.es/actas/downloads/CAEPIA.pdf
Deposited by: Memoria Investigacion
Deposited on: 15 Mar 2018 11:02
Last Modified: 15 Mar 2018 11:02
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