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Computing with feedforward networks of artificial biochemical neurons

By HMM Huub ten Eikelder, SPM Sjoerd Crijns, MN Marvin Steijaert, AML Anthony Liekens and PAJ Peter Hilbers


Phosphorylation cycles are a common motif in biological in-tracellular signaling networks. A phosphorylaton cycle can be modeledas an arti cial biochemical neuron, which can be considered as a variantof the arti cial neurons used in neural networks. In this way the arti cialneural network metaphor can be used to model and study intracellularsignaling networks. The question what types of computations can occurin biological intracellular signaling networks leads to the study of thecomputational power of networks of arti cial biochemical neurons. Herewe consider the computational properties of arti cial biochemical neu-rons, based on mass-action kinetics. We also study the computationalpower of feedforward networks of such neurons. As a result, we give analgebraic characterization of the functions computable by these networks

Publisher: 'Springer Fachmedien Wiesbaden GmbH'
Year: 2009
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Provided by: Repository TU/e
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