  Recurrent temporal dynamics is a phenomenon observed frequently in
high-dimensional complex systems and its detection is a challenging task.
Recurrence quantification analysis utilizing recurrence plots may extract such
dynamics, however it still encounters an unsolved pertinent problem: the
optimal selection of distance thresholds for estimating the recurrence
structure of dynamical systems. The present work proposes a stochastic Markov
model for the recurrent dynamics that allows to derive analytically a criterion
for the optimal distance threshold. The goodness of fit is assessed by a
utility function which assumes a local maximum for that threshold reflecting
the optimal estimate of the system's recurrence structure. We validate our
approach by means of the nonlinear Lorenz system and its linearized stochastic
surrogates. The final application to neurophysiological time series obtained
from anesthetized animals illustrates the method and reveals novel dynamic
features of the underlying system. As a conclusion, we propose the number of
optimal recurrence domains as a statistic for classifying an animals' state of
consciousness.
