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.

研究提出了一种名为 Feed-Forward with delayed Feedback (F$^3$) 的算法，它有效地解决了权重传输和更新锁定等核心问题，实现了生物合理性和计算效率，将生物合理性算法和反向传播算法之间的预测性能差距降低了 96%，为低能耗训练和并行化开辟了新的前景。