Glucose-powered neuroelectronics
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2011
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/66460 |
| _version_ | 1811075959855513600 |
|---|---|
| author | Rapoport, Benjamin Isaac |
| author2 | Rahul Sarpeshkar. |
| author_facet | Rahul Sarpeshkar. Rapoport, Benjamin Isaac |
| author_sort | Rapoport, Benjamin Isaac |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T10:14:08Z |
| format | Thesis |
| id | mit-1721.1/66460 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:14:08Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/664602019-04-10T23:51:56Z Glucose-powered neuroelectronics Rapoport, Benjamin Isaac Rahul Sarpeshkar. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 157-164). A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented. by Benjamin Isaac Rapoport. Ph.D. 2011-10-17T21:28:34Z 2011-10-17T21:28:34Z 2011 2011 Thesis http://hdl.handle.net/1721.1/66460 756043017 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 164 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Rapoport, Benjamin Isaac Glucose-powered neuroelectronics |
| title | Glucose-powered neuroelectronics |
| title_full | Glucose-powered neuroelectronics |
| title_fullStr | Glucose-powered neuroelectronics |
| title_full_unstemmed | Glucose-powered neuroelectronics |
| title_short | Glucose-powered neuroelectronics |
| title_sort | glucose powered neuroelectronics |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/66460 |
| work_keys_str_mv | AT rapoportbenjaminisaac glucosepoweredneuroelectronics |