Structure of the set of feasible neural commands for complex motor tasks.
Structure of the set of feasible neural commands for complex motor tasks.
复制标题
用于复杂运动任务的可行神经命令集的结构。
DOI：
10.1109/embc.2015.7318640
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Gartner,B
中科院分区：
文献类型：
--
作者：
Valero-Cuevas,FJ;Cohn,BA;Szedlak,M;Fukuda,K;Gartner,B
大脑必须从无限的可能性中选择它的控制策略；研究人员认为，它一定是在解决一个最优化问题。虽然这一组可行的解决方案是无限的，位于高维，但它受到运动学、神经肌肉和解剖学限制，大脑必须在这些限制范围内选择最佳解决方案。也就是说，可行激活的集合是结构良好的。然而，到目前为止，还没有方法来描述和量化这些高维解空间的结构。边界框或降维算法不能捕获它们的详细结构。我们提出了一种新的方法，基于计算几何中著名的打了就跑的算法来提取在特定方向上能够产生50%的最大指尖力的可行激活的结构。我们使用了一个逼真的人类食指模型，它有7块肌肉和4个自由度。对于给定的端点处的静力矢量，可行的激活空间是嵌入在7D单位立方体中的三维凸多面体。众所周知，在许多情况下，显式计算该多面体的体积可能变得过于计算复杂。然而，我们的算法能够在可行的激活空间的随机点上对100万个均匀样本进行采样。计算的肌肉激活分布揭示了这些解决方案空间的结构--而不是简单地探索它们的最大和最小值。虽然本文提供了一个食指的7维案例，但我们的方法扩展到了至少有40块肌肉的系统。这将使我们的运动控制社区了解可行的肌肉激活的分布，为未来研究中学习、优化和适应运动模式提供重要的背景信息。
The brain must select its control strategies among an infinite set of possibilities; researchers believe that it must be solving an optimization problem. While this set of feasible solutions is infinite and lies in high dimensions, it is bounded by kinematic, neuromuscular, and anatomical constraints, within which the brain must select optimal solutions. That is, the set of feasible activations is well structured. However, to date there is no method to describe and quantify the structure of these high-dimensional solution spaces. Bounding boxes or dimensionality reduction algorithms do not capture their detailed structure. We present a novel approach based on the well-known Hit-and-Run algorithm in computational geometry to extract the structure of the feasible activations capable of producing 50% of maximal fingertip force in a specific direction. We use a realistic model of a human index finger with 7 muscles, and 4 DOFs. For a given static force vector at the endpoint, the feasible activation space is a 3D convex polytope, embedded in the 7D unit cube. It is known that explicitly computing the volume of this polytope can become too computationally complex in many instances. However, our algorithm was able to sample 1,000,000 uniform at random points from the feasible activation space. The computed distribution of activation across muscles sheds light onto the structure of these solution spaces—rather than simply exploring their maximal and minimal values. Although this paper presents a 7 dimensional case of the index finger, our methods extend to systems with at least 40 muscles. This will allow our motor control community to understand the distributions of feasible muscle activations, providing important contextual information into learning, optimization and adaptation of motor patterns in future research.
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影响因子：
2.4
作者：
Valero-Cuevas FJ;Cohn BA;Yngvason HF;Lawrence EL
通讯作者：
Lawrence EL
DOI：
10.1073/pnas.0500199102
发表时间：
2005-02-22
影响因子：
11.1
作者：
d'Avella, A;Bizzi, E
通讯作者：
Bizzi, E
影响因子：
--
作者：
Valero-Cuevas, Francisco J.
通讯作者：
Valero-Cuevas, Francisco J.
影响因子：
1.9
作者：
Krishnamoorthy, V;Goodman, S;Latash, ML
通讯作者：
Latash, ML
影响因子：
2.4
作者：
Valero-Cuevas, FJ;Zajac, FE;Burgar, CG
通讯作者：
Burgar, CG