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Is There Chaos In The Brain I - Concepts Of Nonlinear Dynamics And Methods Of Investigation.pdf

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Is There Chaos In The Brain I - Concepts Of Nonlinear Dynamics And Methods Of Investigation.pdf

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Is There Chaos In The Brain I - Concepts Of Nonlinear Dynamics And Methods Of Investigation.pdf

文档介绍

文档介绍:. Acad. Sci. Paris, Sciences de la vie / Life Sciences 324 (2001) 773–793
© 2001 Académie des sciences/Éditions scientifiques et médicales Elsevier SAS. Tous droits réservés
S0764446901013774/REV
Point sur / Concise review
Is there chaos in the brain? I. Concepts of nonlinear
dynamics and methods of investigation
Philippe Faure, Henri Korn*
Biologie cellulaire et moléculaire du neurone (Inserm V261), Institut Pasteur, 25 rue Docteur Roux, 75724
Paris Cedex 15, France
Received 18 June 2001; accepted 2 July 2001
Communicated by Pierre Buser
Abstract – In the light of results obtained during the last two decades in a number of
laboratories, it appears that some of the tools of nonlinear dynamics, first developed and
improved for the physical sciences and engineering, are well-suited for studies of
biological phenomena. In particular it has e clear that the different regimes of
activities undergone by nerve cells, neural assemblies and behavioural patterns, the
linkage between them, and their modifications over time, cannot be fully understood in
the context of even integrative physiology, without using these new techniques. This
report, which is the first of two related papers, is aimed at introducing the non expert to
the fundamental aspects of nonlinear dynamics, the most spectacular aspect of which is
chaos theory. After a general history and definition of chaos the principles of analysis of
time series in phase space and the general properties of chaotic trajectories will be
described as will be the classical measures which allow a process to be classified as
chaotic in ideal systems and models. We will then proceed to show how these methods
need to be adapted for handling experimental time series; the dangers and pitfalls faced
when dealing with non stationary and often noisy data will be stressed, and specific
criteria for suspecting determinism in neuronal cells and/or assemblies will be described.
We will finally address two fundamental qu