The neural plausibility of backpropagation has long been disputed, primarily
for its use of non-local weight transport $-$ the biologically dubious
requirement that one neuron instantaneously measure the synaptic weights of
another. Until recently, attempts to create local learning rules that avoid
weight transport have typically failed in the large-scale learning scenarios
where backpropagation shines, e.g. ImageNet categorization with deep
convolutional networks. Here, we investigate a recently proposed local learning
rule that yields competitive performance with backpropagation and find that it
is highly sensitive to metaparameter choices, requiring laborious tuning that
does not transfer across network architecture. Our analysis indicates the
underlying mathematical reason for this instability, allowing us to identify a
more robust local learning rule that better transfers without metaparameter
tuning. Nonetheless, we find a performance and stability gap between this local
rule and backpropagation that widens with increasing model depth. We then
investigate several non-local learning rules that relax the need for
instantaneous weight transport into a more biologically-plausible "weight
estimation" process, showing that these rules match state-of-the-art
performance on deep networks and operate effectively in the presence of noisy
updates. Taken together, our results suggest two routes towards the discovery
of neural implementations for credit assignment without weight symmetry:
further improvement of local rules so that they perform consistently across
architectures and the identification of biological implementations for
non-local learning mechanisms.

本文讨论了反向传播算法的神经可行性，研究了一种竞争性的局部学习规则和几种使权重传输过程更生物学可行的非局部学习规则，并提出了两条无需权重对称的神经实现学分分配的路径。