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Date of Award
Doctor of Philosophy (PhD)
Composed by hundred billion neurons with nonlinearities, the brain consists explicit connection paradigms and exhibits collective dynamics. Also, both high computational flexibility and robustness are crucial for species. How then do biological systems balance the need for both variability and robustness?Mounting evidence suggests that evolution leads neural networks of cerebral cortexto a critical point between order and disorderand yield an optimal trade-off between the robustness and accuracy that biological machinery demands.A rigorous understanding of brain dynamics and function composes multiple levels of organization, suchas neural compartments, neural spiking and network-level population activity. To study thestability of trade-off between robustness and flexibility, we explored whether multi-level neural dynamicschangeor preserveunder visualperturbations and motor learning.We studied neural dynamics under various visual perturbations (visual stimuliand monocular deprivation), and across motor learning in motor cortex.We conducted statistical analysis and used model investigations. We found multiple dynamics changed under perturbation, such as neural connectivity, neural representations, et. al. On network level, criticality either serves as a set-point or constraint. Together, these results contribute to understanding of connection paradigms and collective dynamics in the brain under visual perturbation and motor learning.
Chair and Committee
Anders E. Carlsson, Keith B. Hengen, Zohar Nussinov, Li Yang
Ma, Zhengyu, "Multi-Level Neural Dynamics under Visual Perturbations and Motor Learning" (2019). Arts & Sciences Electronic Theses and Dissertations. 1924.
Available for download on Tuesday, August 15, 2119