Name
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-4-431-88981-6_4 | IWNC |
Keywords | Field | DocType |
phosphorylation cycle,artificial neurons.,cell signaling networks,neural network,artificial neural network,kinetics | Neuroscience,Computer science,Intracellular,Bioinformatics,Neuron,Artificial neural network,Feed forward | Conference |
Citations | PageRank | References |
2 | 0.39 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huub M. M. ten Eikelder | 1 | 11 | 3.96 |
| Sjoerd P. M. Crijns | 2 | 2 | 0.39 |
| Marvin N. Steijaert | 3 | 8 | 1.25 |
| Anthony M. L. Liekens | 4 | 19 | 4.91 |
| Peter A. J. Hilbers | 5 | 100 | 12.73 |