Neuronal Control Mechanisms Underlying Animal Locomotion that Cope with Physical Changes in the Body and Environment

动物运动背后应对身体和环境物理变化的神经元控制机制

• 批准号: 2113528
• 负责人: Tetsuya Iwasaki
• 金额: $ 33.99万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113528&HistoricalAwards=false
• 关键词: Neuronal; Control; Mechanisms; Underlying; Animal

This project will lay a foundation for understanding of biological intelligence, and for the development of engineered systems that can process information, make decisions, and act to achieve desired outcomes. There would be significant societal benefits if the speed and precision of modern machines were matched by flexibility and resilience in responding to unstructured situations -- including those arising from interactions with humans -- rather than relying on preprogrammed instructions. The human brain stands as a model of such flexibility and resilience, and translating the brain's functionality to machines is a flagship challenge for engineering researchers. Due to the complexity of neuronal circuits, however, precise mathematical description of brain functions remains an elusive ambition. This project focuses on a smaller network of neurons within the spinal/nerve cord, called a central pattern generator, with the goal of uncovering the mechanisms underlying a more primitive form of intelligence that underlies animal locomotion. The research outcome will contribute to advancing scientific knowledge of biological information processing and to establishing a technological basis for enabling intelligent machines for various applications including autonomous robots for exploration of unknown environments and therapeutic medical devices for treating neurological dysfunctions. The multi-disciplinary research will be integrated with training and education of diverse student populations.The goal of the project is to pose hypotheses on the design architecture of neuronal control circuits that achieve robust and adaptive behaviors during locomotion, and provide supporting evidence through model-based analyses. An initial working hypothesis is that the neuronal network embeds an internal model of the body-environment dynamics and can be decomposed into a diffusively coupled rhythm generator and a feedforward compensator. The method is based on mathematical modeling of leech swimming, and computational and theoretical analyses using dynamical systems theory. The leech provides one of the best research platforms, because its neuronal circuits are relatively simple and well-studied, yet provide sufficiently rich control functions of engineering relevance. General control principles will be revealed to explain how the neuronal controller maintains or modifies the body oscillation pattern through sensory feedback when environmental changes or neuromechanical failures occur. The outcome of the research is expected to contribute to the foundation of a new control paradigm that integrates trajectory planning and regulation into a distributed network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将为理解生物智能奠定基础，并为开发能够处理信息、做出决策和采取行动以实现预期结果的工程系统奠定基础。如果现代机器的速度和精度与应对非结构化情况（包括与人类互动产生的情况）的灵活性和弹性相匹配，而不是依赖于预先编程的指令，将会产生重大的社会效益。人类大脑是这种灵活性和弹性的典范，将大脑的功能转化为机器是工程研究人员面临的最大挑战。然而，由于神经元回路的复杂性，对大脑功能进行精确的数学描述仍然是一个难以实现的目标。这个项目专注于脊髓/神经索内一个较小的神经元网络，称为中枢模式发生器，其目标是揭示动物运动背后更原始的智能形式的机制。该研究成果将有助于推进生物信息处理的科学知识，并为各种应用的智能机器建立技术基础，包括探索未知环境的自主机器人和治疗神经功能障碍的治疗性医疗设备。多学科研究将与不同学生群体的培训和教育相结合。该项目的目标是对在运动过程中实现鲁棒和自适应行为的神经元控制电路的设计架构提出假设，并通过基于模型的分析提供支持证据。一个初步的工作假设是，神经元网络嵌入了身体-环境动力学的内部模型，可以分解为扩散耦合节律发生器和前馈补偿器。该方法基于水蛭游泳过程的数学建模，并运用动力系统理论进行计算和理论分析。水蛭提供了一个最好的研究平台，因为它的神经回路相对简单，研究得很好，但提供了足够丰富的工程相关控制功能。一般控制原理将被揭示，以解释当环境变化或神经机械故障发生时，神经元控制器如何通过感觉反馈维持或修改身体振荡模式。该研究结果有望为将轨迹规划和调节集成到分布式网络中的新控制范式的建立做出贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Harmonic Balance Analysis of Lur’e Oscillator Network With Non-Diffusive Weak Coupling

• DOI: 10.1109/lcsys.2022.3232408
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Bryan Lee;T. Iwasaki

On Phase Change Rate Maximization with Practical Applications

论相变率最大化与实际应用

• 发表时间: 2023
• 期刊: IFAC World Congress
• 作者: C.-Y. Kao, S. Hara