Artificial Neural Networks have proven, along the last four decades, to be an important tool for modelling of the functional structures of the nervous system, as well as for the modelling of non-linear and adaptive systems in general, both biological and non biological (Haykin, 1999). They also became a powerful biologically inspired general computing framework, particularly important for solving non-linear problems with reduced formalization and structure. At the same time, methods from the area of complex systems and non-linear dynamics have shown to be useful in the understanding of phenomena in brain activity and nervous system activity in general (Freeman, 1992; Kelso, 1995). Joining these two areas, the development of artificial neural networks employing rich dynamics is a growing subject in both arenas, theory and practice. In particular, model neurons with rich bifurcation and chaotic dynamics have been developed in recent decades, for the modelling of complex phenomena in biology as well as for the application in neuro-like computing. Some models that deserve attention in this context are those developed by Kazuyuki Aihara (1990), Nagumo and Sato (1972), Walter Freeman (1992), K. Kaneko (2001), and Nabil Farhat (1994), among others. The following topics develop the subject of Chaotic Neural Networks, presenting several of the important models of this class and briefly discussing associated tools of analysis and typical target applications.
EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks
