Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks. (9th May 2012)

• Record Type: Journal Article
• Authors: Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.
• Other Names: Faes Luca Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2012(2012)
• Publication Date: 2012-05-09
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2012/476324
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : 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.