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.
