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Modeling complex systems by reaction-diffusion cellular nonlinear networks with polynomial weight-functions
Gollas, F.   Tetzlaff, R.  
Inst. of Appl. Phys., Johann Wolfgang Goethe-Univ., Frankfurt, Germany;

This paper appears in: Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Publication Date: 28-30 May 2005
On page(s): 227- 231
ISBN: 0-7803-9185-3
INSPEC Accession Number: 8680939
Digital Object Identifier: 10.1109/CNNA.2005.1543202
Current Version Published: 2005-12-05

Abstract
The treatment of reaction-diffusion differential equations leads to a description of various complex phenomena like nonlinear wave propagation or structure formation, in particular in biological systems. Reaction-diffusion cellular nonlinear networks (RD-CNN) can virtually represent any feature of reaction-diffusion systems. For RD-CNN it has been shown that the existence of locally active cells is a necessary condition for emergent complex behavior (Chua, 1998). In this contribution we use RD-CNN with polynomial reaction terms for modeling complex systems. First results for a RD-CNN modeling a FitzHugh-Nagumo system, with network parameters obtained by a supervised optimization process, are given.

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