Selverston Allen I, Ayers Joseph
Institute for Nonlinear Science, University of California, La Jolla, San Diego, CA, USA.
Biol Cybern. 2006 Dec;95(6):537-54. doi: 10.1007/s00422-006-0125-1. Epub 2006 Dec 7.
In order to determine the dynamical properties of central pattern generators (CPGs), we have examined the lobster stomatogastric ganglion using the tools of nonlinear dynamics. The lobster pyloric and gastric mill central pattern generators can be analyzed at both the cellular and network levels because they are small, i.e., contain only 25 neurons between them and each neuron and synapse are repeatedly identifiable from animal to animal. We discuss how the biophysical properties of each neuron and synapse in the two circuits act cooperatively to generate two different patterns of sequential activity, how these patterns are altered by neuromodulators and perturbed by noise and sensory inputs. Finally, we show how simplified Hindmarsh-Rose models can be made into analog electronic neurons that mimic the lobster neurons and in addition be incorporated into artificial CPGs with robotic applications.
为了确定中枢模式发生器(CPG)的动力学特性,我们使用非线性动力学工具研究了龙虾的口胃神经节。龙虾的幽门和胃磨中枢模式发生器可以在细胞和网络层面进行分析,因为它们体积小,即两者之间仅包含25个神经元,并且每个神经元和突触在不同动物之间都可重复识别。我们讨论了这两个回路中每个神经元和突触的生物物理特性如何协同作用以产生两种不同的顺序活动模式,这些模式如何被神经调质改变以及被噪声和感觉输入干扰。最后,我们展示了如何将简化的Hindmarsh-Rose模型制成模拟龙虾神经元的模拟电子神经元,并且还可以将其纳入具有机器人应用的人工CPG中。