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Started by HARRY R. ERWIN
Fri, 29 Apr 1994 01:49
Chaos in the Brain
Author: HARRY R. ERWIN
Date: Fri, 29 Apr 1994 01:49
Date: Fri, 29 Apr 1994 01:49
45 lines
3493 bytes
3493 bytes
Recently, Lester Ingber asked me what my assessment was of the evidence for and against chaotic dynamics in the brain. I couldn't provide a thoughtful answer at the time, since I had not read the relevant literature. Since then, I've had an opportunity to do that, particularly the Skinner papers and the survey paper by Abarbanel et al on data analysis of observations of possibly chaotic physical systems. My take now is that with certain caveats, I believe the evidence supports the existence of chaotic dynamics in the brain. The first caveat concerns the nature of the data. Any finite set of measurements can be misleading, particularly for nonlinear systems. To demonstrate the existence of a strange attractor, several passes through the attractor are needed at minimum. Second, biological systems are noisy (possibly high-dimensional non-linear dynamics), and that noise can confound the evidence. Third, the dynamics of the brain are non-stationary. The measurement algorithms in common use assume stationarity. Fourth, the attractor in a portion of the brain at any point in time depends on at least three factors: 1) the current afferent signal, 2) the current expectation in terms of which signal patterns are primed for classification, and 3) the local structure of the brain. Hence, you cannot speak of chaotic dynamics in a general sense, but rather of whether the local dynamics ->now<- reflect a strange attractor. Given those caveats, I am of the opinion that Mitra and Skinner (1992, "Low-Dimensional Chaos Maps Learning in a Model Neuropil (Olfactory Bulb)," Integrative Physiological and Behavioral Science, October-December, 1992, Vol. 27, No. 4, 304-322) give reason to believe that the brain can instantiate local attractors with fractal dimension. There appears to be sufficient data collected during the period of existence of individual attractors to allow estimation of the correlation dimension, the estimator algorithm appears to be robust against noise, transients appear not to be an issue (due to the design of the algorithm), and a degree of repeatability appears to be present. Granted, there are difficulties with using real-world data to demonstrate presence of chaos (as Abarbanel, et al., so ably discuss), but Mitra and Skinner appear to have addressed most of those issues. -- Harry Erwin Internet: herwin@gmu.edu Working on Katchalsky nets....
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