From neuron to behavior: dynamic equation-based prediction of biological processes in motor control
From neuron to behavior: dynamic equation-based prediction of biological processes in motor control
复制标题
DOI：
10.1007/s00422-011-0446-6
复制
发表时间：
2011-07-01
影响因子：
1.9
通讯作者：
Bueschges, Ansgar
中科院分区：
文献类型：
--
作者：
Daun-Gruhn, Silvia;Bueschges, Ansgar
本文介绍了使用微分方程形式的连续动态模型来描述和预测生物过程的时间变化，并讨论了它相对于不连续双稳态模型的几个重要优势，以竹节虫行走系统为例。在这个系统中，协调运动是通过协调的关节动力学和不同动力学尺度上的相互作用产生的，因此很难理解。一般来说，使用微分方程进行建模具有包含生物细节的潜力、模拟的适用性，以及最重要的是，可以通过参数操作来预测系统行为。我们将在这篇评论文章中展示，在竹节虫行走系统的情况下，连续动力系统模型如何帮助理解协调运动。
This article presents the use of continuous dynamic models in the form of differential equations to describe and predict temporal changes in biological processes and discusses several of its important advantages over discontinuous bistable ones, exemplified on the stick insect walking system. In this system, coordinated locomotion is produced by concerted joint dynamics and interactions on different dynamical scales, which is therefore difficult to understand. Modeling using differential equations possesses, in general, the potential for the inclusion of biological detail, the suitability for simulation, and most importantly, parameter manipulation to make predictions about the system behavior. We will show in this review article how, in case of the stick insect walking system, continuous dynamical system models can help to understand coordinated locomotion.