Emergence of diverse behaviors from interactions between nonlinear oscillator complex networks and a musculoskeletal system

To understand the relationship between brain structure and behavior in the general movements of fetuses and infants from a complex systems perspective, we investigated how behaviors emerge from interactions between complex networks of nonlinear oscillators and musculoskeletal bodies. We prepared a snake-like robot and some network structures in a physical simulator. The various conditions imposed on the networks were (a) no connection among oscillators, (b) scalefree network, (c) one-dimensional lattice, (d) small-world network, and (e) random network. In the experiments, the robot exhibited multiple crawling and bending behaviors. By estimating the numbers of behavioral attractors, we revealed a qualitative difference between the scale-free network and other complex networks. Introduction Animals and humans exhibit various adaptive behaviors. These behaviors likely emerge from complex interactions among environment, body dynamics, and brain activities, rather than from any single factor, posing several questions. How do the behaviors emerge? What underlying structure shapes such a complex and adaptive interaction? Do diverse interactive behaviors among systems emerge under any specific condition? To answer these questions, we must understand the relationship between structure and functionality in biological entities from a complex systems perspective. Kuniyoshi and Suzuki (2004) proposed a model in which adaptive behaviors emerge through body constraint as a chaotic coupled field. In this model, on the basis of coupled map lattices and globally coupled maps (Kaneko and Tsuda (2003)), transitional adaptive behaviors should emerge as chaotic itinerancy among behavioral attractors. The model connects each chaotic element (logistic map) to a single linear actuator (muscle); that is, the actuator receives the chaotic elements output as a motor command and sends the outputs of its length sensors back to the chaotic element. The body structure behaves as a coupled field of deterministic chaotic activities. However, when the model is executed, the number of emerged behaviors is less than expected, indicating that chaotic itinerancy was suppressed. Sensor