DocumentCode
436364
Title
Flexibility of generalization studies with a neuromolecular model: towards learning generalization
Author
Ugur, A.
Author_Institution
Department of Computer Science, Central Michigan University, Mt. Pleasant, MI 48859 USA
Volume
17
fYear
2004
fDate
June 28 2004-July 1 2004
Firstpage
537
Lastpage
542
Abstract
An abstract neuromolecular neuron model (called the cytomatrix neuron) that illustrates the structure-function plasticity similar to the one in biological organizations is described. The cytomatrix neuron is a softened cellular automaton with molecular components, roughly motivated by interactions that could occur in a molecular or cellular complex. A multiparameter evolutionary algorithm that acts on the various parameters is used for learning. Experiments with various type bit-pattern learning tasks demonstrate that the flexibility of generalization can be increased. This increase in variability of responses can be exploited further. A multiparameter evolutionary algorithm for learning generalization is presented and discussed.
Keywords
Biological system modeling; Biological systems; Biology computing; Computer science; Electronic mail; Evolutionary computation; Learning automata; Neurons; Plastics; Spatiotemporal phenomena; Cytomatrix neuron; Learning generalization; Multiparameter evolutionary learning; Neuromolecular computing; Softened cellular automaton;
fLanguage
English
Publisher
ieee
Conference_Titel
Automation Congress, 2004. Proceedings. World
Conference_Location
Seville
Print_ISBN
1-889335-21-5
Type
conf
Filename
1439422
Link To Document