DETERMINING THE DEGREE OF CHAOS FROM ANALYSIS OF ISI TIME-SERIES IN THE NERVOUS-SYSTEM - A COMPARISON BETWEEN CORRELATION DIMENSION AND NONLINEAR FORECASTING METHODS

Two different chaotic time series analysis methods - the correlation d imension and nonlinear forecasting - are introduced and then used to p rocess the interspike intervals (ISI) of the action potential trains p ropagated along a single nerve fiber of the anesthetized rat. From the results, the conclusion is drawn that compared with the correlation d imension, nonlinear forecasting is more efficient and robust for chaot ic ISI time series analysis in a noisy environment. Moreover, the evol ution of the correlation coefficient curves calculated from nonlinear forecasting can qualitatively give a better reflection of the unpredic tability of the system's future behavior and is in good agreement with the values of the largest Lyapunov exponent that quantitatively measu res the degree of chaos.