Cortical neural population dynamics for flexible motor control and motor learning
Abstract/Contents
- Abstract
- Animals have a remarkable capacity to learn new motor skills while maintaining existing ones. Motor learning encompasses a wide range of phenomena, from relatively low-level mechanisms for calibrating movement parameters, to making high-level cognitive decisions about how to act in a novel environment. Yet the neural mechanisms of these behavioral phenomena, in particular their underlying neural population dynamics, remain largely unknown. Precise motor control and flexible motor learning involve distributed brain regions, among which motor cortex is a key brain hub involved in the control of skilled movements. The neurophysiological studies (Chapters 2 and 3) and the tool-development project (Chapter 4) presented in this dissertation aim to address the roles of neural population activity in motor cortex underlying different behavioral phenomena during movement control and motor learning. In the neurophysiological studies, we recorded hundreds of neurons simultaneously with high-density electrodes while monkeys were learning one or multiple motor skills. To investigate the neural population data, we used techniques based on the dynamical systems theories and applied different dimensionality reduction methods. In the tool-development project, we demonstrate a new imaging platform in behaving monkeys that was able to monitor the same neural population across weeks and could provide us a potential avenue for studying long-term motor learning. In Chapter 2, I first address the question of how neural population activity in rhesus macaque motor cortex changes when learning new arm movement from dynamic force perturbations. Chapter 3 reports the neural population activity patterns during interference and learning of multiple different skills. Chapter 4 demonstrates the development of chronic in vivo two-photon calcium imaging for rhesus macaques generating arm movements and the implementation of an optical brain-computer interface (oBCI), as well as the capability to combine functional and structural imaging of the motor cortical neural population.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Sun, Xulu |
|---|---|
| Degree supervisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Thesis advisor | Clandinin, Thomas R. (Thomas Robert), 1970- |
| Thesis advisor | Luo, Liqun |
| Thesis advisor | Newsome, William T |
| Thesis advisor | Shen, Kang, 1972- |
| Degree committee member | Clandinin, Thomas R. (Thomas Robert), 1970- |
| Degree committee member | Luo, Liqun |
| Degree committee member | Newsome, William T |
| Degree committee member | Shen, Kang, 1972- |
| Associated with | Stanford University, Department of Biology |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xulu Sun. |
|---|---|
| Note | Submitted to the Department of Biology. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/xj107pd6601 |
Access conditions
- Copyright
- © 2021 by Xulu Sun
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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