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Chaos in Brain Function pp 74–82Cite as

Self-Similarity in Hyperchaotic Data

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Abstract

The brain is a giant dynamic system. Its complexity should exceed e.g. that of a dripping faucet (Rössler 1977), which is one of the work-horses of nonlinear science (Martien and Shaw 1985). There are numerical methods for estimating the embedding dimension of a signal generated by a dynamic system (Froehling et al. 1981) which have been successfully applied to many kinds of data including brain-generated ones (see Mayer-Kress 1986 for many references).

Originally published in Başar E, Bullock TH (eds) Brain dynamics. Springer, Berlin Heidelberg New York, pp 113-121 (Springer series in brain dynamics, vol 2). Cross references refer to that volume.

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Authors
  1. O. E. Rössler
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  2. J. L. Hudson
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Editor information

Editors and Affiliations

  1. Institute of Physiology, Medical University Lübeck, Ratzeburger Allee 160, D-2400, Lübeck 1, Germany

    Erol Başar

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© 1990 Springer-Verlag Berlin Heidelberg

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Rössler, O.E., Hudson, J.L. (1990). Self-Similarity in Hyperchaotic Data. In: Başar, E. (eds) Chaos in Brain Function. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-75545-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-75545-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-52329-1

  • Online ISBN: 978-3-642-75545-3

  • eBook Packages: Springer Book Archive

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