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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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非线性神经动力学调制用于闭环深度大脑刺激系统.

Yanyu Li, Maryam M Shanechi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    我们开发了一个新的神经网络框架,准确地模拟复杂的大脑动态,用于先进的闭环深度大脑刺激 (DBS). 这种方法在控制神经系统疾病方面优于线性模型.

    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.
    • 生物医学工程 生物医学工程

    背景情况:

    • 对神经动态的准确建模对于推进闭环神经调节系统至关重要.
    • 目前的闭环深度大脑刺激 (DBS) 通常依赖于简单的值,限制其有效性.
    • 线性状态空间模型 (LSSM) 难以捕捉神经动力学固有的非线性.

    研究的目的:

    • 设计和评估能够捕捉非线性神经动态的高级建模和神经调节策略.
    • 为了比较基于 Koopman 操作员的方法和基于神经网络的方法在闭环 DBS 模拟中的性能.
    • 为了证明这些方法对未来闭环神经调节系统开发的潜力.

    主要方法:

    • 基于库普曼运算符的方法被设计为在高维线性空间中表示非线性系统.
    • 开发了一个基于循环神经网络 (RNN) 的框架,以捕捉非线性动态模式.
    • 为了反控制,RNN框架与代线性二次调节器 (iLQR) 相结合.

    主要成果:

    • 库普曼操作员方法在模拟复杂的神经动态方面存在局限性,原因是观察函数选择和数据约束.
    • 与LSSM和Koopman运营商相比,RNN-iLQR框架在系统识别和控制准确性方面表现优越.

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  • 生物启发的闭环DBS模拟验证了RNN-iLQR方法的有效性.

    • 结论:

    • RNN-iLQR框架为模拟闭环神经调节中的复杂非线性神经动态提供了一个有前途的解决方案.
    • 这种方法有可能显著提高DBS疗法的疗效和精度.
    • 这种方法的进一步发展可能会导致神经和神经精神疾病的更复杂和个性化的治疗方法.