Modeling the role of the basal ganglia in motor control and motor programming

• Main Author: Mao, Zhi-Hong, 1972-
• Other Authors: Steve G. Massaquoi and Eric M. Feron.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2006
• Subjects: Harvard University > MIT Division of Health Sciences and Technology.
• Online Access: http://hdl.handle.net/1721.1/34971

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2005. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Includes bibliographical references (p. 153-166). === The basal ganglia (BG) are a group of highly interconnected nuclei buried deep in the brain. They are involved in an important range of brain functions, including both lower-level movement control and higher-level cognitive decision making. Dysfunction of the BG has been linked to human neurological disorders such as Parkinson's disease, Huntington's disease, and schizophrenia. This thesis proposes a unified functional model of the BG, called multi-input multi-output adaptive switching (MIMOAS) model that attempts to account for the role of the BG in both higher-level rote behavior and lower-level motor control. In the model, BG circuitry effectively implements a large set of parallel noncompetitive logical OR and NOR circuits that can be driven by specific patterns of cortical activity. These afford selective gating of target thalamocortical neurons. This process can be viewed as a general mapping between binary context and response vectors. The mapping is proved to be learnable via a reinforcement mechanism that is consistent with actions commonly proposed for nigro-striatal dopaminergic pathways in the striatum and homeostasis in synaptic physiology. It appears that the cortico-striatal connections provide a biologically plausible realization of winner-take-all dynamics that is different from many engineering alternatives implementing the same function. With the winner-take-all units as functioning as a hidden layer, using corticostriatal weights as the only tunable parameters, the adaptive BG network can develop the capacity to perform universal binary mappings. In this way, the model can simulate important features of procedural learning in human experiments. At the same time, it can be shown that derangement of the winner-take-all dynamics could underlie the tremor and rigidity seen in Parkinson's disease. === by Zhi-Hong Mao. === Ph.D.