Abstract
In this paper, we propose a method of constructing a central pattern generator (CPG) model. A CPG is a network of neurons capable of generating a rhythmic output. In this method, the CPG model can generate undulating patterns. These generated patterns depend on the network topology of the CPG model. Furthermore, transitions between the different undulating patterns arise from changes of the network energy. We can consider the network energy as some driving intention of locomotion. We model CPGs as graphs, and introduce two time-evolution systems; one is a wave equatin which is Hamilton system and the other is a gradient system. The former generates intrinsic oscillating modes, and they correspond to gait patterns naturally. And the latter selects and transits in modes by bifurcating its potential functional. The superposed system of them is the objective CPG model. Some computer simulations show that the proposed CPG model with twelve oscillators (teice of six legs) can generate hexapodal gait patterns. And transitions between gait patterns are accomplished in the simulations.
