Paper
30 December 2008 Dynamical complexity in a mean-field model of human EEG
Federico Frascoli, Mathew P. Dafilis, Lennaert van Veen, Ingo Bojak, David T. J. Liley
Author Affiliations +
Proceedings Volume 7270, Biomedical Applications of Micro- and Nanoengineering IV and Complex Systems; 72700V (2008) https://doi.org/10.1117/12.813966
Event: SPIE Smart Materials, Nano- and Micro-Smart Systems, 2008, Melbourne, Australia
Abstract
A recently proposed mean-field theory of mammalian cortex rhythmogenesis describes the salient features of electrical activity in the cerebral macrocolumn, with the use of inhibitory and excitatory neuronal populations (Liley et al 2002). This model is capable of producing a range of important human EEG (electroencephalogram) features such as the alpha rhythm, the 40 Hz activity thought to be associated with conscious awareness (Bojak & Liley 2007) and the changes in EEG spectral power associated with general anesthetic effect (Bojak & Liley 2005). From the point of view of nonlinear dynamics, the model entails a vast parameter space within which multistability, pseudoperiodic regimes, various routes to chaos, fat fractals and rich bifurcation scenarios occur for physiologically relevant parameter values (van Veen & Liley 2006). The origin and the character of this complex behaviour, and its relevance for EEG activity will be illustrated. The existence of short-lived unstable brain states will also be discussed in terms of the available theoretical and experimental results. A perspective on future analysis will conclude the presentation.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Federico Frascoli, Mathew P. Dafilis, Lennaert van Veen, Ingo Bojak, and David T. J. Liley "Dynamical complexity in a mean-field model of human EEG", Proc. SPIE 7270, Biomedical Applications of Micro- and Nanoengineering IV and Complex Systems, 72700V (30 December 2008); https://doi.org/10.1117/12.813966
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Cited by 3 scholarly publications.
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KEYWORDS
Electroencephalography

Neurons

Mathematical modeling

Chaos

Brain

Action potentials

Data modeling

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