Kenji Doya, Shuji Yoshizawa
Animal locomotion patterns are controlled by recurrent neural networks called central pattern generators (CPGs). Although a CPG can oscillate autonomously, its rhythm and phase must be well coordinated with the state of the physical system using sensory inputs. In this paper we propose a learning algorithm for synchronizing neural and physical oscillators with specific phase relationships. Sensory input connections are modified by the correlation between cellular activities and input signals. Simulations show that the learning rule can be used for setting sensory feedback connections to a CPG as well as coupling connections between CPGs.
1 CENTRAL AND SENSORY MECHANISMS IN
Patterns of animal locomotion, such as walking, swimming, and fiying, are generated by recurrent neural networks that are located in segmental ganglia of invertebrates and spinal cords of vertebrates (Barnes and Gladden, 1985). These networks can produce basic rhythms of locomotion without sensory inputs and are called central pattern generators (CPGs). The physical systems of locomotion, such as legs, fins, and wings combined with physical environments, have their own oscillatory char(cid:173) acteristics. Therefore, in order to realize efficient locomotion, the frequency and the phase of oscillation of a CPG must be well coordinated with the state of the physical system. For example, the bursting patterns of motoneurons that drive a leg muscle must be coordinated with the configuration of the leg, its contact with the ground, and the state of other legs.