Flexibility of Generalization Studies with a Neuromolecular Model: Towards Learning Generalization

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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.

Original languageEnglish
Title of host publicationSoft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Proceedings of the Sixth Biannual World Automation Congress, WAC 2004
EditorsM. Jamshidi, M. Reuter, D. Andina, J.S. Jamshidi
Pages537-542
Number of pages6
StatePublished - 2004
EventSoft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004 - Sevilla, Spain
Duration: Jun 28 2004Jul 1 2004

Publication series

NameSoft Computing with Industrial Applications - Proceedings of the Sixth Biannual World Automation Congress

Conference

ConferenceSoft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004
Country/TerritorySpain
CitySevilla
Period06/28/0407/1/04

Keywords

  • Cytomatrix neuron
  • Learning generalization
  • Multiparameter evolutionary learning
  • Neuromolecular computing
  • Softened cellular automaton

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