Identical feedforward + backprop math as the 2D original (sigmoid activation, per-weight gradient update), just extended into a real third dimension: the coloured surface below the network is the live output of feedforward([x1,x2]) plotted as height over the whole input plane, so you watch the XOR decision boundary physically rise and fold as training progresses.