利用逼真的肌肉骨骼模型对肌肉模块进行协同质量评估,以确定学习成绩

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-05-30 DOI:10.3389/fncom.2024.1355855
Akito Fukunishi, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe
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引用次数: 0

摘要

我们的中枢神经系统如何有效控制复杂的肌肉骨骼系统仍存在争议。肌肉协同作用假说假定存在协调多块肌肉的功能神经模块,从而简化了这一复杂系统。基于肌肉协同作用的模块化可以促进运动学习,同时又不影响任务的完成。然而,模块化在运动控制中的有效性仍存在争议。这种模糊性可能部分源于忽略了模块化的性能取决于相关模块的机械方面,如模块施加的扭矩。为了解决这个问题,本研究根据运动学习研究中常用的性能指标:扭矩产生的准确性和学习速度,引入了两个标准来评估模块集的质量。其中一个标准评估模块产生机械扭矩方向的规律性,另一个标准评估其大小的均匀性。为了验证我们的标准,我们使用前馈神经网络模拟了上臂真实肌肉骨骼系统的扭矩产生任务的运动学习,同时改变了控制条件。我们发现,所提出的标准成功地解释了各种控制条件下学习成绩的变化趋势。这些结果表明,所使用模块的机械扭矩方向的规律性和大小的均匀性是决定学习成绩的重要因素。虽然这些标准最初是为基于错误的学习方案而设计的,但这种追求哪组模块更适合运动控制的方法对其他一般模块化研究具有重要意义。
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Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model
How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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