BAC: Neural Optimization Algorithms in Respiratory Control

这项研究的目的是揭示哺乳动物大脑在特定运动中动态优化和控制运动输出的神经策略。任务。研究人员将研究哺乳动物呼吸运动输出的自适应控制的神经计算算法，并开发基于该动态最优控制问题的Hebbian学习算法。这项工作将集中于神经模型和算法的开发，以适应广泛的生理和神经生理学数据描述呼吸系统的特征。要解决的具体方面是：1)用于两个无穷大的神经结构和自适应算法-时间和有限时间动态控制；2)系统不确定性的影响及其自适应补偿：3)系统不稳定的原因和镇定方法；以及4)用于提高网络响应的稳健性和容错性、噪声抑制和精度的神经策略。这项工作是重要的，因为它可能导致一种新的范例，为设计智能控制系统的广泛范围工程和机器人应用。

The objective of this research is to uncover the neural strategies utilized by the mammalian brain for dynamic optimization and control of motor output in a specific motor task. The investigator will study the neural computation algorithms underlying the adaptive control of respiratory motor output in mammals and develop neural-network based Hebbian learning algorithms for this dynamic optimal control problem. The work will focus on the development of neural models and algorithms to fit the extensive physiological and neurophysiological data that characterize the respiratory system. The specific aspects to be addressed are: 1) the neural architectures and adaptive algorithms to use for both infinite-time and finite-time dynamic control; 2) the effects of system uncertainties and their adaptive compensation; 3) causes for system instabilities and methods for stabilization; and 4) neural strategies for increasing robustness and fault tolerance, noise rejection, and precision of the network response. This work is important since it may lead to a novel paradigm for the design of intelligent control systems for a wide range of engineering and robotic applications.