小脑的一个模型在以反射为基础的神经肌肉骨模型中产生了步态适应,这是在分带行走期间发生的
Sophie Fleischmann1, Julian Shanbhag2, Joerg Miehling2
1Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nürnberger Straße 74, Erlangen, 91052, Germany.
bioRxiv : the preprint server for biology
|December 17, 2025
概括
小脑的新计算模型成功模拟了分带跑步机行走期间的步态适应,显示了步长不对称的减少. 这种模型有助于更好地理解运动控制和适应机制.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 分带跑步机行走揭示了神经类型人类和小脑损伤的人之间的运动适应差异.
- 目前的神经肌肉骨模拟无法预测适应分裂带行走干扰的情况.
研究的目的:
- 开发和测试一个小脑的计算模型与脊柱反射模型集成.
- 为了研究小脑在分带行走期间基于错误的运动适应中的作用.
主要方法:
- 将脊柱反射模型扩展到一个生物启发的小脑模型,用于基于错误的适应.
- 模拟80年代的分带跑步机步行 (2:1比) 与扩展模型.
- 专注于小脑对摇摆启动时间的调制.
主要成果:
- 扩展模型将步长不对称性 (SLA) 指数地适应到对称性,与反射模型不同.
- 适应率和趋同水平各不相同,显示出灵活性.
- 通过调整摇摆启动时间来实现SLA适应,而不会改变反射增益.
结论:
- 与脊柱反射模型集成的计算小脑模型可以在分带行走期间驱动前进步态适应.
- 这种模型推进了预测模拟,并有助于理解运动控制和适应.
- 模块化框架允许测试在连续扰动下对运动控制的假设.
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