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Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks

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http://hdl.handle.net/2183/20793
Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España
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Title
Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks
Author(s)
Alvarellos, Alberto
Pazos, A.
Porto-Pazos, Ana B.
Date
2012
Citation
Alvarellos-González A, Pazos A, Porto-Pazos AB. Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks. Comput Math Methods Med. 2012; 2012 [10 p.]
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.
Keywords
Algorithms
Astrocytes
Cell communication
Neurological models
Nerve net
Neural networks (Computer)
Neurons
 
Editor version
http://dx.doi.org/10.1155/2012/476324
Rights
Atribución 3.0 España
ISSN
1748-670X
1748-6718
 

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