Abstract:
:Two different chaotic time series analysis methods--the correlation dimension and nonlinear forecasting--are introduced and then used to process the interspike intervals (ISI) of the action potential trains propagated along a single nerve fiber of the anesthetized rat. From the results, the conclusion is drawn that compared with the correlation dimension, nonlinear forecasting is more efficient and robust for chaotic ISI time series analysis in a noisy environment. Moreover, the evolution of the correlation coefficient curves calculated from nonlinear forecasting can qualitatively give a better reflection of the unpredictability of the system's future behavior and is in good agreement with the values of the largest Lyapunov exponent that quantitatively measures the degree of chaos.
journal_name
Biol Cybernjournal_title
Biological cyberneticsauthors
Gong Y,Xu J,Ren W,Hu S,Wang Fdoi
10.1007/s004220050422subject
Has Abstractpub_date
1998-02-01 00:00:00pages
159-65issue
2eissn
0340-1200issn
1432-0770journal_volume
78pub_type
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