Experimental and computational explorations of different forms of plasticity in motor learning and stroke recovery

Motor learning and the neural plasticity that underlies it are essential ingredients in allowing us to perform almost everything we do on a daily basis, from driving a car to typing on a keyboard. Additionally, proper understanding of these phenomena would allow us to harness them to aid those who suffer motor impairments resulting from stroke or other diseases. This thesis lays out the work done during my PhD aimed at improving this understanding. The work falls under two main projects. The first proposes a neural network model of sensory cortex to explore the roles of Hebbian and homeoplasticity after stroke, including how they interact to determine the optimal time to initiate rehabilitation. The second project then endeavors to strengthen a central assumption of the model, namely that purely unsupervised Hebbian learning can occur in the sensorimotor system independent of performance feedback (error or reward) from the environment. This is accomplished through arm reaching experiments that assay for unsupervised learning using behavioral measurements of use-dependent learning. This second project is also extended to explore the phenomenon of use-dependent learning in general to determine whether it could play a significant role alongside error- and reward-based learning mechanisms in shaping motor control. The organization of the thesis is as follows. Chapter 2 gives a background of the literature that is pertinent to understanding models of cortical organization, behavioral and physiological aspects of stroke recovery, and the mechanisms of motor learning. Chapter 3 and 4 then detail the two projects described above. These chapters may be read independently of the others and are formatted as manuscripts for submission to particular journals, as per Neuroscience Graduate Program guidelines. Finally, Chapter 5 summarizes the work and hypothesizes about its links to other recent work.