Neural prosthetics for paralysis : algorithms and low-power analog architectures for decoding neural signals

This thesis develops a system for adaptively and automatically learning to interpret patterns of electrical activity in neuronal populations in a real-time, on-line fashion. The system is primarily intended to enable the long-term implantation of low-power, microchip-based recording and decoding hardware in the brains of human patients in order to treat neurologic disorders. The decoding system developed in the present work interprets neural signals from the parietal cortex encoding arm movement intention, suggesting that the system could function as the decoder in a neural prosthetic limb, potentially enabling a paralyzed person to control an artificial limb just as the natural one was controlled, through thought alone. The same decoder is also used to interpret the activity of a population of thalami neurons encoding head orientation in absolute space. The success of the decoder in that context motivates the development of a model of generalized place cells to explain how networks of neurons adapt the configurations of their receptive fields in response to new stimuli, learn to encode the structure of new parameter spaces, and ultimately retrace trajectories through such spaces in the absence of the original stimuli.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Physics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Rapoport, Benjamin Isaac

Advisor dc:contributor.advisor

• Rahul Sarpeshkar.

• Physics.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/39289

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/39289