A Neuronal Model of Central Pattern Generator to Account for Natural Motion Variation

作者：Razavian Reza Sharif^*; Mehrabi Naser^*; McPhee John^*

来源：Journal of Computational and Nonlinear Dynamics, 2016, 11(2): 021007.

DOI：10.1115/1.4031086

We have developed a simple mathematical model of the human motor control system, which can generate periodic motions in a musculoskeletal arm. Our motor control model is based on the idea of a central pattern generator (CPG), in which a small population of neurons generates periodic limb motion. The CPG model produces the motion based on a simple descending command-the desired frequency of motion. Furthermore, the CPG model is implemented by a spiking neuron model; as a result of the stochasticity in the neuron activities, the motion exhibits a certain level of variation similar to real human motion. Finally, because of the simple structure of the CPG model, it can generate the sophisticated muscle excitation commands much faster than optimization-based methods.

• 出版日期2016-3