Harry R. Erwin (1994) On Chaotic EEG Dynamics. Psycoloquy: 5(34) Eeg Chaos (8)

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PSYCOLOQUY (ISSN 1055-0143) is sponsored by the American Psychological Association (APA).
Psycoloquy 5(34): On Chaotic EEG Dynamics

ON CHAOTIC EEG DYNAMICS
Commentary on Wright, Kydd & Liley on EEG-chaos

Harry R. Erwin
Department of Computer Science and Institute
for Computational Sciences and Informatics
George Mason University
Fairfax, VA 22030, USA

herwin@gmu.edu

Abstract

Wright, Kydd & Liley's (1993) article attempting to harmonize locally chaotic EEG dynamics with globally linear dynamics does not address Mitra and Skinner's (1992) evidence for nonstationary temporal signals in limit set dimension. That phenomenon requires a more sophisticated model of global EEG dynamics for its explanation.

Keywords

chaos, EEG simulation, neurodynamics
1. Wright, Kydd & Liley (1993) present an approach to harmonizing Freeman's (1991) model of chaotic local dynamics with Wright's (1990) larger-scale model predicting linear dynamics. Their approach does not seem to address the evidence for nonstationary limit set dimension as a function of expectation and afferent signals (Mitra & Skinner, 1992). That phenomenon suggests that the EEG dynamics of the Wright, Kydd and Liley model may hide significant large-scale temporal and spatial heterogeneity in cortical function. Tsuda's point (1994) that interacting chaotic microsystems can produce macroscopic chaos is suggestive in this context.

2. Wright et al.'s suggestion that chaos is present in local dynamics appears supportable. In addition to Mitra & Skinner's work, my own simulation work (Erwin, 1994, following Freeman and Yao, 1990) shows microscopic chaos and suggests at least two roles for it within the olfactory system: stochastic resonance and modulation of parallel signal channels to avoid illusionary correlations. Despite those positive results, my modeling is far from the biology and should only be regarded as suggestive. In particular, I have not addressed training, a key deficiency, given the role of expectation in my simulation and in Mitra & Skinner's data.

3. A macroscopic model of EEG should address some issues that arise specifically at the transition from locally chaotic dynamics to the macroscopic scale. My simulation results here are again similar to Mitra and Skinner's data: the system appears to enter a low dimensional state in response to the presentation of a known pattern. An unanswered question here is whether the pyriform cortex is responding to the direct measurement of synchronized efferent data streams or to an indirect identification of the partially coherent state attained by the olfactory bulb. The latter will reduce the efferent data rate in vivo. This will have some influence on the global EEG model.

REFERENCES

Erwin, H.R. (1994) The Application of Katchalsky Network Models to Radar Pattern Classification. In: Origins: Brain and Self-Organization (Proceedings of the Second Appalachian Neurodynamics Conference), ed. K. Pribram. INNS Press, LEA.

Freeman, W.J., and Yao, Y. (1990) Model of Biological Pattern Recognition with Spatially Chaotic Dynamics. Neural Networks, Volume 3, Number 2, 153-170.

Freeman, W.J. (1991) Predictions on neocortical dynamics derived from studies in paleocortex. In: Induced rhythms of the brain, eds. E. Basar & T.H. Bullock. Cambridge, MA, Birkhaeuser Boston Inc.

Mitra, M. & Skinner, J.E. (1992) Low-Dimensional Chaos Maps Learning in a Model Neuropil (Olfactory Bulb). Integrative Physiological and Behavioral Science, Volume 27, Number 4, 304-322.

Tsuda, I. (1994) From Micro-Chaos to Macro-Chaos: Chaos can Survive even in Macroscopic States of Neural Activities. PSYCOLOQUY 5(12) eeg-chaos.3.tsuda.

Wright, J.J. (1990) Reticular Activation and the Dynamics of Neuronal Networks. Biol. Cybern. 62:289-298.

Wright, J.J., Kydd, R.R. & Liley, D.T.J. (1993) EEG Models: Chaotic and Linear. PSYCOLOQUY 4(60) eeg-chaos.1.wright.


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