Spinal cord injury therapy through active learning

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.

The Art of Madness

The nineteenth century was not an entirely kind time for the female artist. Coming of age as the 1800’s bridged into its latter half, literary artists Elizabeth Stuart Phelps Ward, Charlotte Perkins Gilman, and Kate Chopin were all well aware of their uncharitable culture. Equipped with firm feminist bents and creative visions, each of three women produced a seminal work – The Story of Avis, “The Yellow Wallpaper,” and The Awakening, respectively – taking that atmosphere to task. In these stories, each of the three women produces a female protagonist who struggles for having been born simultaneously an artist and a woman. The writers pit their women’s desires against the restrictive latitude of their time and show how such conditions drive women to madness, as a result of which they are forced to either escape into the blind mind of insanity or deal daily with their pain and inescapable societal condemnation. In an age where “hysteria” was a frequent hit in the vernacular, Phelps, Gilman and Chopin use art and literature as mediums to show that, indeed, there is a method behind the madness.