嵌入式感应运动控制:运动神经控制的计算建模
Muhammad Noman Almani1,2, John Lazzari1,3, Jeffrey D Walker1,3
11Center for Neurocomputation and Machine Intelligence, Wu Tsai Institute, Yale University, New Haven, Connecticut, USA;
这篇评论探讨了大脑如何通过整合神经人口活动,最佳反和身体生物力学来控制运动. 了解这些相互作用是推进感觉运动控制模型的关键.
科学领域:
- 神经科学是一个神经科学.
- 发动机控制器的控制器
- 计算神经科学是一种神经科学.
背景情况:
- 感官运动控制依赖于神经群体,反机制和身体生物力学之间的复杂相互作用.
- 现有的模型往往侧重于单个组件,需要采用综合方法.
研究的目的:
- 审查和综合目前对传感运动控制的理解.
- 突出神经活动,最佳控制原则和生物力学之间的相互作用.
- 确定未来的研究方向,以建立一个全面的运动控制理论.
主要方法:
- 对解剖循环,神经群体动态,最佳控制理论和体内控制的文献综述.
- 综合了关于感觉运动信号处理和运动行为的研究结果.
- 讨论通过肌肉骨动力学阐明神经活动的最新进展.
主要成果:
- 感官运动信号涉及连接皮质,皮下区域和脊髓的分布式循环.
- 在运动规划和执行期间的神经群体活动的特点是低维的,不断发展的多元体.
- 最佳控制理论为理解内部模型和电机控制中的反提供了一个框架.
结论:
- 神经运动控制的综合性解释需要结合来自神经群体的见解,最佳反和生物力学.
- 未来的研究应该解决多任务处理,认知丰富的行为,多区域电路模型和适当的解剖细节水平.
- 嵌入式感应运动控制的进步对于弥合当前理论差距至关重要.
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