Researchers have introduced PC-ALM, a local alternative to backpropagation that uses layer-local feedback control systems to distribute supervision credit throughout neural networks. Each layer is equipped with a dynamical system to propagate this credit without relying on global network dynamics. Despite this localized approach, PC-ALM can train deep residual networks up to 1000 layers while nearly matching the performance of traditional backpropagation.