Advancing motor neural prosthesis robustness and neuroscience
Abstract/Contents
- Abstract
- The frontier challenges that must be solved before brain-machine interfaces (BMIs) can be used as clinically useful motor prostheses differ depending on the degree of function being restored. Two-dimensional cursor control (i.e., for communication) has recently reached high levels of peak performance in pre-clinical studies, but translation is hampered by less than reliable performance due to unstable neural signals. Meanwhile, control of robotic arms remains poor, despite some impressive glimpses at what the future could be, because we lack fundamental understanding of how the brain incorporates the BMI into its motor schema. This hampers our ability to accurately decode intended arm movements. My dissertation focused on both sets of problems in pre-clinical macaque BMI studies. Chapters 2 and 3 provide solutions for improving BMI robustness. I first describe a machine learning approach to building decoder algorithms that are robust to the changing neural-to-kinematic mappings that plague translational BMI efforts. We developed a multiplicative recurrent neural network decoder that could exploit the large quantities of data generated by a chronic BMI — data that has heretofore gone unused. I then describe a neural engineering approach for increasing the device lifespan by providing high performance control even after losing spike signals. I developed a method for decoding local field potentials (LFPs) as a longer-lasting alternative or complimentary BMI control signal. This led to the highest-performing LFP-driven BMI and the first 'hybrid' BMI which decoded kinematics from spikes and LFPs together. Chapter 4 looks ahead to challenges that will be encountered when BMI-controlled limbs operate in the physical world by describing how error signals impact ongoing BMI control. I perturbing the kinematics of monkeys performing a BMI cursor task and found that visual feedback drove responses starting 70 ms later in the same motor cortical population driving the BMI. However, this initial response did not cause unwanted BMI output because it was limited to a decoder null space in which activity does not affect the BMI. When activity changed in output-potent dimensions starting 115 ms after perturbation, it caused corrective BMI movement. This elegant arrangement may hint at a broader computational strategy by which error processing is separated from output.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Stavisky, Sergey D |
|---|---|
| Associated with | Stanford University, Neurosciences Program. |
| Primary advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Thesis advisor | Newsome, William T |
| Thesis advisor | Norcia, Anthony Matthew |
| Thesis advisor | Raymond, Jennifer L |
| Advisor | Newsome, William T |
| Advisor | Norcia, Anthony Matthew |
| Advisor | Raymond, Jennifer L |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sergey D. Stavisky. |
|---|---|
| Note | Submitted to the Neurosciences Program. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Sergey Stavisky
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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