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Type of Document	Dissertation
Author	Cai, Lance Lin-Lan
Author's Email Address	lancecai AT caltech.edu
URN	etd-08142006-165844
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-08142006-165844
Title	Robotics training algorithms for optimizing motor learning in spinal cord injured subjects
Degree	PhD
Option	Bioengineering
Advisory Committee	Advisor Name Title Joel Wakeman Burdick Committee Chair Morteza Gharib Committee Member Richard A. Andersen Committee Member V. Reggie Edgerton Committee Member Yaser S. Abu-Mostafa Committee Member
Keywords	Spinal Cord Injury Motor Learning Machine Learning Controls Robotics
Date of Defense	2006-07-19
Availability	unrestricted
Abstract The circuitries within the spinal cord are remarkably robust and plastic. Even in the absence of supraspinal control, such circuitries are capable of generating functional movements and changing their level of excitability based on a specific combination of properceptive inputs going into the spinal cord. This has led to an increase in locomotor training, such as Body Weight Support Treadmill training (BWST) for spinal cord injured (SCI) patients. However, today, little is known about the underlying physiological mechanisms responsible for the locomotor recovery achieved with this type of rehabilitative training, and the optimal rehabilitative strategy is still unknown. This thesis describes a mouse model to study the effect of rehabilitative training on SCI. Using this model, the effects of locomotor recovery on adult spinal mice following complete spinal cord transaction is examined. Results that indicate adult spinal mice can be robotically trained to step, and when combined with the administration of quipazine (a broad serotonin agonist), there is an interaction and retention effect. Results also demonstrate that the training paradigm can be optimized in using “Assisted-as-Needed” (AAN) training. To find the optimal AAN training parameters, a learning model is developed to test the effect of various parameters of the AAN training algorithm. Simulation results from our model show that learning is training-dependent. In addition, the model predicts that improved motor learning can improve post-SCI by making the AAN training more adaptable. The primary contributions of this thesis are twofold, in biology and engineering. We develop a mouse model using novel robotic devices and controls that can be used to study SCI and other locomotor disorders in the future by taking advantage of the many different strains of transgenic mice that are commercially available. We also further confirm that sensory integration responsible for motor control is distributed throughout the hierarchy of the neuromuscular system and can be achieved within the isolated spinal cord. Lastly, by developing a learning model, we can start looking into how variability plays a role in motor learning, the understanding of which will have profound implications in neurophysiology, machine learning and adaptive optimal controls research.
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