Supplementary Material GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

The GAIT -prop and ITP targets are implemented as a weak perturbation of the forward pass. The table below presents the relevant parameters.Parameter V alue Learning Rate of Adam Optimiser {10 The results report peak and final (end of training) accuracy on the training set (organise'peak / final'). Parameters shown in bold were chosen and used for all results presented in the main paper. We find that target propagation often does best when early-stopping is implemented to'catch' this peak, unlike the other two algorithms which have asymptotic In the main paper, we showed that GAIT -propagation produces networks with final training/test accuracies which are indistinguishable from those produced by backpropagation of error. The performance of deep multi-layer perceptrons trained by BP, and GAIT -prop.