Computing with feedforward networks of artificial biochemical neurons
| Autor: | Eikelder, ten, H.M.M., Crijns, S.P.M., Steijaert, M.N., Liekens, A.M.L., Hilbers, P.A.J., Suzuki, Y., Hagiaya, M., Umeo, H., Adamatzky, A. |
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| Přispěvatelé: | Computational Biology |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Artificial neural network Computer science Quantitative Biology::Molecular Networks fungi Computer Science::Neural and Evolutionary Computation Feed forward food and beverages Bioinformatics Quantitative Biology::Cell Behavior Quantitative Biology::Subcellular Processes medicine.anatomical_structure nervous system medicine Neuron Biological system |
| Zdroj: | Natural Computing ISBN: 9784431889809 IWNC Natural Computing : proceedings of the 2nd International Workshop on Natural Computing, Nagoya Japan, 38-47 STARTPAGE=38;ENDPAGE=47;TITLE=Natural Computing : proceedings of the 2nd International Workshop on Natural Computing, Nagoya Japan |
| ISSN: | 1867-2914 |
| DOI: | 10.1007/978-4-431-88981-6_4 |
| Popis: | Phosphorylation cycles are a common motif in biological intracellular signaling networks. A phosphorylaton cycle can be modeled as an artificial biochemical neuron, which can be considered as a variant of the artificial neurons used in neural networks. In this way the artificial neural network metaphor can be used to model and study intracellular signaling networks. The question what types of computations can occur in biological intracellular signaling networks leads to the study of the computational power of networks of artificial biochemical neurons. Here we consider the computational properties of artificial biochemical neurons, based on mass-action kinetics. We also study the computational power of feedforward networks of such neurons. As a result, we give an algebraic characterization of the functions computable by these networks. |
| Databáze: | OpenAIRE |
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