• 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