The well-known backpropagation learning algorithm is probably the most popular learning algorithm in artificial neural networks. It has been widely used in various applications of deep learning. The backpropagation algorithm requires a separate feedback network to back propagate errors. This feedback network must have the same topology and connection strengths (weights) as the feed-forward network. In this article, we propose a new learning algorithm that is mathematically equivalent to the backpropagation algorithm but does not require a feedback network. The elimination of the feedback network makes the implementation of the new algorithm much simpler. The elimination of the feedback network also significantly increases biological plausibility for biological neural networks to learn using the new algorithm by means of some retrograde regulatory mechanisms that may exist in neurons. This new algorithm also eliminates the need for two-phase adaptation (feed-forward phase and feedback phase). Hence, neurons can adapt asynchronously and concurrently in a way analogous to that of biological neurons.

中文翻译：

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神经网络中的监督学习：无反馈网络的实现和生物学合理性


众所周知的反向传播学习算法可能是人工神经网络中最流行的学习算法。它已广泛应用于深度学习的各种应用中。反向传播算法需要一个单独的反馈网络来反向传播误差。该反馈网络必须具有与前馈网络相同的拓扑和连接强度（权重）。在本文中，我们提出了一种新的学习算法，该算法在数学上等同于反向传播算法，但不需要反馈网络。反馈网络的消除使得新算法的实现变得更加简单。反馈网络的消除还显着增加了生物神经网络通过神经元中可能存在的一些逆行调节机制来学习新算法的生物学合理性。这种新算法还消除了两阶段自适应（前馈阶段和反馈阶段）的需要。因此，神经元可以以类似于生物神经元的方式异步且同时地适应。