Figure 6.

Distribution alignment methods for stabilizing movement decoders across days and subjects. A, Data dimensionality is first reduced, and then low-dimensional projections are aligned onto a previously recorded movement distribution. B, KL-divergence provides a robust metric for alignment (displayed as a function of the angle used to rotate the data). Many local minima exist (points 1, 2, 3, 4), which makes alignment difficult. C, Prediction accuracy of 2-D kinematics for distribution alignment decoding and supervised methods. Left, Accuracy of DAD using movements from Subject M (DAD-M), from Subject C (DAD-C), and using movements from both Subjects M and C (DAD-MC). Right, Standard L2-regularized supervised decoder (Sup) and a combined decoder (Sup-DAD), which averages the results of the supervised and DAD decoders. All results are compared with an Oracle decoder (far right), which provides an upper bound for the best linear decoding performance for this task. Adapted from Dyer et al., 2017. D, A schematic of a generative adversarial network strategy for distribution alignment across multiple days: generator network (left) receives new data and learns a transformation of the data to match the prior (from a previous day).