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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 318758, 11 pages
Research Article

Single Gaussian Chaotic Neuron: Numerical Study and Implementation in an Embedded System

Universidad Autónoma de Nuevo León, UANL, FIME, Avenida Universidad S/N Ciudad Universitaria, 66451 San Nicolás de los Garza Nuevo León, NL, Mexico

Received 26 October 2012; Revised 28 December 2012; Accepted 3 January 2013

Academic Editor: Eric Campos Canton

Copyright © 2013 Luis M. Torres-Treviño et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Artificial Gaussian neurons are very common structures of artificial neural networks like radial basis function. These artificial neurons use a Gaussian activation function that includes two parameters called the center of mass (cm) and sensibility factor ( ). Changes on these parameters determine the behavior of the neuron. When the neuron has a feedback output, complex chaotic behavior is displayed. This paper presents a study and implementation of this particular neuron. Stability of fixed points, bifurcation diagrams, and Lyapunov exponents help to determine the dynamical nature of the neuron, and its implementation on embedded system illustrates preliminary results toward embedded chaos computation.