Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks

Abstract

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the “information networks” different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Induced Synchronization of Chaos-Chaos Intermittency Maintaining Asynchronous State of Chaotic Orbits by External Feedback Signals

“…With respect to the study of the application of coupled chaotic oscillators [43]- [45], Park et al demonstrated that the movement patterns of snake-like robots are produced by chaotic coupled oscillators [45]. This oscillation exhibits chaotic itinerancy [46] which is the transition dynamics switching between the chaotic synchronized and desynchronized states.…”

Macroscopic Cluster Organizations Change the Complexity of Neural Activity

“…How the brain network is self-organized through interactions with the environment, and how it influences behaviors are cutting-edge topics of research in developmental science. Several studies have used computational modelling and shown the importance of the interaction between the body and brain to exhibit diverse behavior [ 39 , 40 ], to self-organize the brain [ 41 ], or to learn a task [ 42 , 43 ]. Furthermore, several studies have shown the low complexity of brain activity or the abnormal structure of a functional network during a task [ 44 ] while watching a video [ 45 ] or based on the video-EEG data [ 46 ].…”

Designing spontaneous behavioral switching via chaotic itinerancy

“…We show that our method can realize CI characterized by the random transition of a finite number of quasi-attractors as shown in (19,21). Some classes of CI, however, are difficult to design even with our method.…”

“…In this study, we propose an algorithm, freely designing both the trajectories of quasi-attractors and transition rules among them in a setup of high-dimensional chaotic dynamical systems. We aim to design the properties of CI characterized by a finite state machine and finite switching time, such as those in the neurorobotics context (19,21). We prepare transition rules described by a Markov model and aim to emulate them through CI using a high-dimensional nonlinear dynamical system.…”