Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson’s disease
Fig 9
Minimum beta power found by each algorithm as a function of iteration.
BayesOpt (blue) is compared against the Nelder-Mead (orange) and DIRect (green) algorithms, with the shaded region indicating the standard deviation. Each algorithm was run 1000 times in all 7 parameter combinations, and compared for their ability to find the global minimum in as few function evaluations as possible. BayesOpt and DIRect perform comparably in all cases, while NM falls behind in cases where power threshold is optimized. The dotted lines represent the global minimum beta power, as well as the beta power with DBS OFF and cDBS for comparison.