Liu Q, Wang J Z
School of Electric Engineering, Huaihai Institute of Technology, Lian Yungang, 222005, China.
Biol Cybern. 2018 Aug;112(4):345-356. doi: 10.1007/s00422-018-0758-x. Epub 2018 Apr 26.
Experimental data have shown that inherent bursting of the neuron plays an important role in the generation of rhythmic movements in spinal networks. Based on the mechanism that the spinal neurons of a lamprey generate this inherent bursting, this paper builds a simplified inherent bursting neuron model. A new locomotion control neural network is built that takes advantage of this neuron model and its performance is analyzed mathematically and by numerical simulation. From these analyses, it is found that the new control networks have no restriction on their topological structure for generating the oscillatory outputs. If a network is used to control the motion of bionic robots or build the model of the vertebrate spinal circuitry, its topological structure can be constructed using the unit burst generator model proposed by Grillner. The networks can also be easily switched between oscillatory and non-oscillatory output. Additionally, inactivity and saturation properties of the new networks can also be developed, which will be helpful to increase the motor flexibility and environmental adaptability of bionic robots.
实验数据表明，神经元的固有爆发在脊髓网络节律性运动的产生中起着重要作用。基于七鳃鳗脊髓神经元产生这种固有爆发的机制，本文构建了一个简化的固有爆发神经元模型。利用该神经元模型构建了一种新的运动控制神经网络，并对其性能进行了数学分析和数值模拟。通过这些分析发现，新的控制网络在产生振荡输出时对其拓扑结构没有限制。如果用一个网络来控制仿生机器人的运动或构建脊椎动物脊髓电路模型，其拓扑结构可以使用格里尔纳提出的单位爆发发生器模型来构建。这些网络还可以很容易地在振荡输出和非振荡输出之间切换。此外，新网络的不活动和饱和特性也可以得到发展，这将有助于提高仿生机器人的运动灵活性和环境适应性。
