1. A neurostimulator device comprising: a stimulation assembly connectable to a plurality of electrodes, wherein the plurality of electrodes are configured to stimulate a spinal cord using an applied complex stimulation pattern; one or more sensors configured to measure a response related to stimulation of the spinal cord; and at least one processor configured to modify the applied complex stimulation pattern deliverable by the plurality of electrodes to create a modified complex stimulation pattern for subsequent stimulation of the spinal cord by integrating data from the one or more sensors and performing a machine learning method implementing a Gaussian Process Optimization (“GPO”) relation that describes a predicted mean and a variance of a motor performance function for a plurality of candidate complex stimulation patterns, including the applied complex stimulation pattern, based on at least on one of (i) previous data from the one or more sensors, and (ii) data derived in a previous stimulation study, wherein the GPO relation includes an upper confidence bound rule for applying a weight to modify the applied complex stimulation pattern based on a number of times data is received from the one or more sensors regarding stimulation of the spinal cord, and wherein the upper confidence bound rule modifies the applied complex stimulation pattern through convergence of the GPO relation toward an optimal candidate complex stimulation pattern.