  Chaotic itinerancy is a frequently observed phenomenon in high-dimensional
and nonlinear dynamical systems, and it is characterized by the random
transitions among multiple quasi-attractors. Several studies have revealed that
chaotic itinerancy has been observed in brain activity, and it is considered to
play a critical role in the spontaneous, stable behavior generation of animals.
Thus, chaotic itinerancy is a topic of great interest, particularly for
neurorobotics researchers who wish to understand and implement autonomous
behavioral controls for agents. However, it is generally difficult to gain
control over high-dimensional nonlinear dynamical systems. Hence, the
implementation of chaotic itinerancy has mainly been accomplished
heuristically. In this study, we propose a novel way of implementing chaotic
itinerancy reproducibly and at will in a generic high-dimensional chaotic
system. In particular, we demonstrate that our method enables us to easily
design both the trajectories of quasi-attractors and the transition rules among
them simply by adjusting the limited number of system parameters and by
utilizing the intrinsic high-dimensional chaos. Finally, we quantitatively
discuss the validity and scope of application through the results of several
numerical experiments.
