Designing spontaneous behavioral switching via chaotic itinerancy
This work addresses the problem of implementing autonomous behavioral controls for agents in neurorobotics, offering a more systematic approach compared to heuristic methods, though it appears incremental in advancing control techniques for chaotic systems.
The study tackled the challenge of controlling high-dimensional nonlinear dynamical systems to implement chaotic itinerancy, a phenomenon linked to spontaneous behavioral switching, by proposing a novel method that allows reproducible design of quasi-attractor trajectories and transition rules through parameter adjustments and intrinsic chaos, with validation via numerical experiments.
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.