μSim:通过肌肉骨模型阐明神经控制运动的目标驱动框架
bioRxiv : the preprint server for biology
|February 26, 2024
概括
这项研究将运动皮质 (MC) 模型作为一个循环神经网络 (RNN) 控制器,以了解大脑如何产生适应性运动. 该模型准确地解码神经活动,并揭示了灵活运动控制的计算原理.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 运动皮层 (MC) 在动态环境中控制复杂的运动.
- 了解有目的和可概括的运动的神经动态是一个关键的挑战.
研究的目的:
- 使用目标驱动的方法来建模运动皮层 (MC).
- 为了阐明运动控制背后的神经动力学.
- 开发一个框架来理解灵活的电机控制.
主要方法:
- 制定了MC作为一个循环神经网络 (RNN) 控制器.
- 利用生物精确的肌肉骨模型与实时感官反.
- 采用深度强化学习来训练RNN在神经和肌肉骨限制下.
- 综合目标驱动和数据驱动的建模,使用记录的神经活动.
主要成果:
- 经过训练的RNN模型准确地解码了实验记录的神经群体动态和单个单元MC活动.
- 该模型展示了对测试条件的概括性,这些条件与培训不同.
- 同时目标和数据驱动的建模增强了解码精度和通用性.
- 该框架成功阐明了用于灵活运动控制的神经动力学的计算原理.
结论:
- 开发的RNN框架提供了关于神经动力学如何使灵活的运动控制成为可能的见解.
- 这个计算模型准确地复制和解码神经活动.
- 该框架可适应未来对电机控制的实验研究.
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相关概念视频
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Like neurons, muscle cells are also regarded as excitable due to their capacity to change in response to stimuli, primarily due to voltage-gated ion channels embedded in their plasma membranes, which get activated by alterations in the cell's...
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Excitation-Contraction Coupling in Skeletal Muscles
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When an action potential...
When an action potential...
Muscle Coordination and Action
Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement.
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