Error backpropagation is an extremely effective algorithm for assigning credit in artificial neural networks. However, weight updates under Backprop depend on lengthy recursive computations and require separate output and error messages -- features not shared by biological neurons, that are perhaps unnecessary. In this paper, we revisit Backprop and the credit assignment problem. We first decompose Backprop into a collection of interacting learning algorithms; provide regret bounds on the performance of these sub-algorithms; and factorize Backprop's error signals. Using these results, we derive a new credit assignment algorithm for nonparametric regression, Kickback, that is significantly simpler than Backprop. Finally, we provide a sufficient condition for Kickback to follow error gradients, and show that Kickback matches Backprop's performance on real-world regression benchmarks.

本文中，我们对反向传播（Backprop）算法以及信用分配问题进行了重新审视，提出了一个新的算法——Kickback。通过对其性能设立担保限度，我们分解反向传播为一系列交互式的学习算法，并将其误差信号分解。最后，我们给出了Kickback跟随误差梯度所需的充分条件，并证明了其在真实世界回归基准测试中的表现。

Kickback削减反向传播的红色条条: 神经网络中生物学可行的信用分配