Integral to this learning process is the so-called backpropagation-of-error algorithm, often known as backprop.

这个学习过程不可或缺的部分是所谓的误差反向传播算法，通常称为 backprop。

Biological neurons, for their part, send signals of fixed strengths, which the backprop algorithm is not designed to deal with.

就生物神经元而言， 它们发送固定强度的信号，而反向传播算法并不是为处理这种信号而设计的。

All the same, the success of neural networks has renewed interest in whether some kind of backprop happens in the brain.

尽管如此，神经网络的成功重新引起了人们对大脑中是否存在某种反向传播的兴趣。

Designing experiments to prove whether backprop, or any other algorithm, is at play in the brain is surprisingly tricky.

设计实验来证明反向传播或任何其他算法是否在大脑中发挥作用是极其棘手的。

In conventional backprop, error signals lead to adjustments in the synapses, which in turn cause changes in neuronal activity.

在传统的反向传播中， 误差信号会导致突触的调整，进而引起神经元活动的变化。

Some researchers have also made alterations to the backprop algorithm to allow it to process spikes rather than continuous signals.

一些研究人员还对反向传播算法进行了修改，使其能够处理尖峰信号而不是连续信号。

Scientists working at the boundary between neuroscience and AI have also shown that small tweaks to backprop can make it more brain-friendly.

研究神经科学和人工智能边界的科学家也表明，对反向传播进行微小的调整可以使其更加适合大脑。

In a paper published in Nature Neuroscience earlier this year, Yuhang Song and colleagues at Oxford University laid out a method that flips backprop on its head.

今年早些时候，牛津大学的宋宇航和同事在《自然神经科学》杂志上发表的一篇论文中提出了一种颠覆反向传播的方法。

When the authors tested out prospective configuration in artificial neural networks they found that they learned in a much more human-like way—more robustly and with less training—than models trained with backprop.

当作者测试人工神经网络中的前瞻性配置时，他们发现与使用反向传播训练的模型相比，它们的学习方式更像人类，而且更加稳健，所需训练更少。

If artificial neural networks can be given biologically realistic features, such as specialised neurons that can integrate activity and error signals in different parts of the cell, then backprop can occur with a single set of neurons.

如果人工神经网络能够具备生物学上的真实特征，比如能够整合细胞不同部分的活动和错误信号的专门神经元，那么反向传播就可以用一组神经元进行。