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从神经元到行为:基于动态方程的运动控制生物学过程预测。

From neuron to behavior: dynamic equation-based prediction of biological processes in motor control.

作者信息

Daun-Gruhn Silvia, Büschges Ansgar

机构信息

Emmy Noether Research Group of Computational Biology, Department of Animal Physiology, Institute of Zoology, University of Cologne, Germany.

出版信息

Biol Cybern. 2011 Jul;105(1):71-88. doi: 10.1007/s00422-011-0446-6. Epub 2011 Jul 19.

Abstract

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.

摘要

本文介绍了以微分方程形式使用连续动态模型来描述和预测生物过程中的时间变化,并讨论了其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进行说明。在这个系统中,协调的运动是由不同动态尺度上的协同关节动力学和相互作用产生的,因此很难理解。一般来说,使用微分方程进行建模具有纳入生物学细节的潜力、适合模拟,最重要的是,可以通过参数操纵来预测系统行为。在这篇综述文章中,我们将展示在竹节虫行走系统的情况下,连续动态系统模型如何有助于理解协调的运动。

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