We introduce $\mu$PC, a reparameterisation of predictive coding networks that enables stable training of 100+ layer ResNets on simple tasks with hyperparameter transfer.

Our work effectively bridges Hebbian principles with explicit representation learning objectives, demonstrating considerable potential and biological plausibility.

We propose a novel local learning approach for covariate selection in nonparametric causal effect estimation, which accounts for the presence of latent variables.