Overview

There has been a heated debate about whether chaos theory can be applied to the dynamics of the human brain. While it is obvious that nonlinear mechanisms are crucial in neural systems, there has been strong criticism of attempts to identify at strange attractors in brain signals and to measure their fractal dimensions, Lyapunov exponents, etc. Conventional methods analyzing brain dynamics are largely based on linear models and on Fourier spectra. Regardless of the existence of strange attractors in brain activity, the neurosciences should benefit greatly from alternative methods that have been developed in recent years for the analysis of nonlinear and chaotic behavior.Contents: Cortical Dynamics Experiments and Models (S Rotter & A Aertsen) Is Nonlinearity Evident in Time Series of Brain Electrical Activity? (T Schreiber) Finding and Characterizing Unstable Fixed Points by Controlling System Dynamics (D T Kaplan) Detection of Phase Locking from Noisy Data: Application to Magnetoencephalography (M Rosenblum et al.) Dynamical Analysis in Clinical Practice (P E Rapp & T I Schmah) Rhythms of the Brain: Between Randomness and Determinism (F H Lopes da Silva et al.) Pre-ictal Changes of the EEG Dynamics in Epileptic Patients: Clinical and Neurobiological Implications (M Baulac et al.) Spatio-Temporal Dynamics of Epileptogenic Networks (M Le van Quyen et al.) Pre-ictal Changes and EEG Analyses Within the Framework of Lyapunov Theory (H R Moser et al.) Epilepsy When Chaos Fails (J C Sackellares et al.) Possible Clinical and Research Applications of Nonlinear EEG Analysis in Humans (K Lehnertz et al.) Dynamics of EEG Signals During Petit-Mal Epileptic Seizures (R Friedrich) Detection of Epileptic Dynamics in Neuromagnetic Signals: Spectral Analyses Versus Characteristics of Correlation Function (E Bohl et al.) Nonlinear Methods for Evoked Potential Analyses and Modeling (B H Jansen) From Slow Potentials to Chaos: Processing in the Brain and Controlling the Brain (H Prei l & W Lutzenberger) and other papersReadership: Neural scientists, physicists, statisticians and mathematicians interested in applying nonlinear dynamical system theory to brain research.

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