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Chaos in Brain Function and the Problem of Nonstationarity: A Commentary

  • G. J. Mpitsos

Abstract

Over 16 years ago, the work of two of the participants in the present conference clearly pointed to the need to consider the brain as a noisy processor (Adey 1972) in which statistical mechanisms (John 1972) lead to the production of organized behavior. The difficulty in pursuing this problem further in terms of the individual neurons that comprise the network has been in the lack of conceptual tools with which to understand the informational language arising from simple neuro-anatomic structures such as the convergence of two neurons onto a third (Mpitsos et al. 1978), or, as Sperry (1981) has stated it, in our inability to handle the “three-bodies problem.” Bullock (1984) has observed that “circuit analysis,” the once great hope of neurobiology, will not by itself “provide the major insights necessary to understand the emergent mechanisms present in complex systems,” even if these systems are as simple as the stomatogastric ganglion of the lobster (Selverston 1980). The realization of this fact itself is perhaps a major achievement in invertebrate neurobiology. Nonetheless, while major strides have been made in understanding the self-organizing processes in distributed, parallel networks (e.g., Amit et al. 1985; Grossberg 1980; Hopfield 1982; Jeffrey and Rosner 1986; Kleinfeld and Sompolinsky 1987; Pellionisz and Llinas 1983; Rumhelhart et al. 1986; Sejnowski and Rosenberg 1987; Werbos 1974), the subject of noise has not been broadly addressed, and only recently has it received rekindled interest.

Keywords

Lyapunov Exponent Phase Portrait Correlation Dimension Chaotic Attractor Motor Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • G. J. Mpitsos

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