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. 2012:2012:476324.
doi: 10.1155/2012/476324. Epub 2012 May 9.

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

Alberto Alvarellos-González  1 , Alejandro Pazos, Ana B Porto-Pazos
Affiliations

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

Alberto Alvarellos-González et al. Comput Math Methods Med. 2012.

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.

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Figures

Figure 1
Figure 1
Artificial Neural Network structure.
Figure 2
Figure 2
Structure of an artificial neuron.
Figure 3
Figure 3
Artificial Neuron-glia network structure.
Figure 4
Figure 4
Astrocyte representation.
Figure 5
Figure 5
Flow chart and pseudocode of the five Neuron-Glia algorithms.
Figure 6
Figure 6
Global processing effect flowchart and pseudocode.
Figure 7
Figure 7
MUX device.
See this image and copyright information in PMC

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