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Date of Award

Summer 8-15-2019

Author's School

Graduate School of Arts and Sciences

Author's Department

Physics

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

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.

Language

English (en)

Chair and Committee

Ralf Wessel

Committee Members

Anders E. Carlsson, Keith B. Hengen, Zohar Nussinov, Li Yang

Comments

Permanent URL: https://doi.org/10.7936/569v-wh86

Available for download on Tuesday, August 15, 2119

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