Ongoing studies have identified similarities between neural representations
in biological networks and in deep artificial neural networks. This has led to
renewed interest in developing analogies between the backpropagation learning
algorithm used to train artificial networks and the synaptic plasticity rules
operative in the brain. These efforts are challenged by biologically
implausible features of backpropagation, one of which is a reliance on
symmetric forward and backward synaptic weights. A number of methods have been
proposed that do not rely on weight symmetry but, thus far, these have failed
to scale to deep convolutional networks and complex data. We identify principal
obstacles to the scalability of such algorithms and introduce several
techniques to mitigate them. We demonstrate that a modification of the feedback
alignment method that enforces a weaker form of weight symmetry, one that
requires agreement of weight sign but not magnitude, can achieve performance
competitive with backpropagation. Our results complement those of Bartunov et
al. (2018) and Xiao et al. (2018b) and suggest that mechanisms that promote
alignment of feedforward and feedback weights are critical for learning in deep
networks.

研究深度人工神经网络中的反向传播学习算法与大脑神经元突触可塑性规律的类比，介绍了不依赖于对称前向和后向突触权重的算法，提出通过加强权重符号一致性的反馈对准法的修改，可以实现与反向传播法相当的性能。这些研究结果表明，促进前向和反馈权重对准的机制对于深度网络的学习是至关重要的。