Abstract.

Previously in paper [1] we investigated chaotic dynamics of the simplified Walter Freeman KIII model. As mentioned in [2] this type of neural networks could be applied to solve different problems like pattern recognition and time series prediction. This makes it at least theoretically useful in the realm of computer networks security and discovery of computer attacks. In the present work we consider more complex and detailed version of this model. The main result of this article is numerical and bifurcation analysis of chaotic dynamics in detailed version of KIII network. The main focus is on chaotic attractors corresponding to basic state of the system. These attractors were originally discovered in the works of Walter Freeman and co-authors [3], [4]. Bifurcation scenario in KIII network discovered in the present work contributed to our knowledge about structure of these so-called ‘basic’ attractors.

Keywords:

chaotic dynamics, nonlinear dynamics, neural networks, KIII model, attractor, differential equations, delay differential equations.

PP. 44-53.

References.

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