Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks
type: Item
instance of: scholarly article
title: Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks
zbMATH Open document ID: 1239.92011
DOI: 10.1155/2012/476324
author: Alberto Alvarellos-González
author: Alejandro Pazos
author: Ana B. Porto-Pazos
published in: Computational \& Mathematical Methods in Medicine
publication date: 2012-06-19T00:00:00Z
review text: Summary: 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 functions 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.
Mathematics Subject Classification ID: 92C20
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 92-08
zbMATH DE Number: 6047846
Wikidata QID: Q42175923
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/476324
OpenAlex ID: W2047050427
cites work: A logical calculus of the ideas immanent in nervous activity
cites work: Neural networks and physical systems with emergent collective computational abilities.
cites work: A New Approach to the Extraction of ANN Rules and to Their Generalization Capacity Through GP
DBLP publication ID: journals/cmmm/GonzalezPP12
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description: scientific article; zbMATH DE number 6047846