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首页> 外文学位 >Biologically Plausible Control of Fast Reaching Movements Using Non-Traditional Cost Functions.
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Biologically Plausible Control of Fast Reaching Movements Using Non-Traditional Cost Functions.

机译：使用非传统成本函数的快速到达运动的生物合理控制。

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摘要

Optimal control has been used as a technique to uncover mathematical principles which are observed regularly in the dynamics of human movement. We present two new models of human reaching movements. While both are rooted in optimal control theory, the models were conceived by questioning basic tenets and typical practices used in optimal control as applied to human movement. In the first model, we use cost functions that measure various control signals via the Linfinity norm as opposed to the commonly used L2 norm. Doing so models human reaching movements as well as current approaches, but results in control signals that can be reasoned about in terms of neural spikes and their timing. In the second model, we change the organization of the terms within a single, multi-term cost function by transforming it into many single-term cost functions. This approach yields sub-optimal results with regard to cost, yet produces equal or better results when applied to accuracy in modeling human reaching movements. The traditional optimal control approach to modeling human movement assumes that humans have an optimal design in terms of the anatomy and physiology of their motor systems. This design is assumed to optimally minimize costs such as energy consumption, or error while attaining a goal. However, it is more likely that in changing environments/niches, humans and other animals are still evolving, and therefore have not yet arrived at an optimal design. By reorganizing the terms of a cost function in a cost-suboptimal way, while achieving high accuracy with regard to modeling the movements, we challenge the basic premise of cost-optimality that underlies optimal control based models of human movement. Additionally, this reorganization of cost function terms into multiple cost functions results in multiple, interacting control signals, making it possible to combine the these signals in ways that resemble the connectivity of the human motor system, which contains a diverse set of neural signals working in concert, each with its own character and purpose. For this reason, we introduce a framework that generalizes these concepts, which can be utilized for further modeling of human movement. The framework expands upon traditional optimal control as applied to modeling human movements by supporting multiple interacting control signals. This allows for experiments which more closely resemble the neural architecture of the motor system, thereby making it easier to reason about experimental results in terms of the construction of the human motor system.
机译：最佳控制已被用作揭示数学原理的技术，而数学原理是在人体运动动力学中经常观察到的。我们提出了两种人类伸手运动的新模型。虽然两者都植根于最佳控制理论，但这些模型是通过质疑用于人类运动的最佳控制中的基本原理和典型实践来构思的。在第一个模型中，我们使用成本函数，该函数通过Linfinity规范来测量各种控制信号，这与常用的L2规范相反。这样做可以模拟人类的伸手动作以及当前的进路，但是会产生可以根据神经尖峰及其时序进行推理的控制信号。在第二个模型中，我们通过将其转换为许多单项成本函数来更改单项，多项成本函数内的术语组织。这种方法在成本方面产生次优的结果，但当应用于对人类伸手动作进行建模的准确性时，则会产生相同或更好的结果。传统的模拟人体运动的最佳控制方法是假设人体在其运动系统的解剖学和生理学方面具有最佳设计。假定此设计可以在达到目标的同时将成本（例如能源消耗或错误）最佳地最小化。但是，在不断变化的环境/环境中，人类和其他动物仍在进化，因此尚未达到最佳设计的可能性更高。通过以次优成本的方式重组成本函数的各项，同时在实现运动建模方面实现了高精度，我们挑战了基于成本最优性的基本前提，后者是基于最优控制的人类运动模型的基础。此外，将成本函数项重组为多个成本函数会产生多个相互作用的控制信号，从而有可能以类似于人类运动系统的连通性的方式组合这些信号，其中包含了一组在神经网络中工作的神经信号。音乐会，各有特色和目的。因此，我们引入了一个概括这些概念的框架，可将其用于人类运动的进一步建模。该框架扩展了传统的最佳控制，该控制通过支持多个交互控制信号而应用于模拟人体运动。这允许进行与电机系统的神经体系结构更为相似的实验，从而使根据人体电机系统的构造更容易得出实验结果。

著录项

  • 作者

    Gamble, Geoffrey George.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Computer science.;Physiological psychology.;Neurosciences.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 148 p.
  • 总页数 148
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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