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Spinal cord injury therapy through active learning

Citation

Desautels, Thomas Anthony (2014) Spinal cord injury therapy through active learning. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:07252013-120308708

Abstract

Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:machine learning; Gaussian process; spinal cord injury; active learning; regret bound; rodent; theoretical; applied
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Minor Option:Computation and Neural Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Burdick, Joel Wakeman
Thesis Committee:
  • Burdick, Joel Wakeman (chair)
  • Tai, Yu-Chong
  • Beck, James L.
  • Edgerton, V. Reggie
Defense Date:1 July 2013
Additional Information:Code for the algorithms implemented in Chapter 3 of this dissertation appears at http://www.its.caltech.edu/~tadesaut/ and at http://las.ethz.ch/publications.php?all .
Funders:
Funding AgencyGrant Number
NIH/NINDSR01NS062009
NIH/NIBIBR01EB007615
Christopher and Dana Reeve FoundationESH1-2012(JB)
ThinkSwiss Research ScholarshipUNSPECIFIED
Record Number:CaltechTHESIS:07252013-120308708
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:07252013-120308708
Related URLs:
URLURL TypeDescription
http://www.its.caltech.edu/~tadesaut/AuthorPersonal website, with code access
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7918
Collection:CaltechTHESIS
Deposited By: Thomas Desautels
Deposited On:01 Aug 2013 02:35
Last Modified:13 Apr 2015 19:00

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PDF (Complete dissertation) - Final Version
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PDF (Forematter) - Final Version
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PDF (Chapter 1: Introduction) - Final Version
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