Abstract
A single chaotic neuron can be developed using a single neuron with a Gaussian activation function and feeding back the output to its input. The Gaussian activation function has two parameters, the center of mass and the width of the bell called sensibility factor. The change of these parameters determines the behavior of the single neuron that could be stationary but more of the times is dynamic inclusive chaotic. This paper presents an implementation of this single neuron in an embedded system generating a set of bifurcation plots illustrating the dynamic complexity that could have this simple system.
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González-Estrada, L., González-Sanmiguel, G., Torres-Treviño, L.M., Rodríguez, A. (2013). Implementation of a Single Chaotic Neuron Using an Embedded System. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_25
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DOI: https://doi.org/10.1007/978-3-642-37798-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37797-6
Online ISBN: 978-3-642-37798-3
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