Hierarchical Bayesian optimization of targeted motor outputs with spatiotemporal neurostimulation
The idea for this thesis by article sprung from the following question: can we use neural prostheses to stimulate specific muscles in order to help recovery of motor control after stroke or cervical injury? This question is of crucial importance to 15 million people each year around the globe, and is at the heart of Numa Dancause and Marco Bonizzato’s research, our collaborators in the Neuroscience department at the University of Montreal. It is now possible to implant large capacity electrodes for electrical stimulation in cortex, but still difficult to predict their effect on the brain and the rest of the body. Nevertheless, preliminary but promising results on rats and monkeys have shown that a non-negligible motor recovery is obtained after stimulation of regions of motor cortex that are still functional. The difficulties related to optimal microcortical stimulation hence consist in finding both one of these regions, and a stimulation protocol with optimal recovery efficacy. This search has up to present day been performed by hand, but recent and upcoming large scale stimulation technologies permitting delivery of spatio-temporal signals are making such exhaustive searches impossible.A promising approach to automating and optimizing this discovery is the use of Bayesian optimization. My work has consisted in developing and refining such techniques with two scientific questions in mind: (1) how can we evoke complex movements by chaining cortical microstimulations?, and (2) can these outperform single channel stimulations in terms of recovery efficacy? We present in the main article of this thesis our hierarchical Bayesian optimization approach which uses gaussian processes to exploit known properties of the brain to speed up the search, as well as first results answering question 1. We leave to future work a definitive answer to the second question.
BCI Stimulation Corticale Cortical Stimulation Gaussian Processes Bayesian Optimization