  Backpropagation has long been criticized for being biologically implausible,
relying on concepts that are not viable in natural learning processes. This
paper proposes an alternative approach to solve two core issues, i.e., weight
transport and update locking, for biological plausibility and computational
efficiency. We introduce Feed-Forward with delayed Feedback (F$^3$), which
improves upon prior work by utilizing delayed error information as a
sample-wise scaling factor to approximate gradients more accurately. We find
that F$^3$ reduces the gap in predictive performance between biologically
plausible training algorithms and backpropagation by up to 96%. This
demonstrates the applicability of biologically plausible training and opens up
promising new avenues for low-energy training and parallelization.
