Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 476324, 10 pages
Research Article

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

Departamento de Tecnologías de la Información y las Comunicaciones, Facultad de Informática, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, Spain

Received 14 October 2011; Revised 12 January 2012; Accepted 25 February 2012

Academic Editor: Luca Faes

Copyright © 2012 Alberto Alvarellos-González et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.