Such artificial networks are pieces of software composed of virtual neurons.

这种人工网络由不同的软件组成虚似的神经元。

That's why more recent methods for understanding the Stroop effect include developing artificial networks to mimic what's going on.

这就是为什么最近研究 Stroop 效应的方法包含了开发人工网络来模拟正在发生的事情。

Most notable are artificial neural networks, which were inspired by neurons in our brains!

最著名的是人工神经网络，它的灵感来自我们大脑中的神经元！

Researchers at Purdue University are using artificial neural networks, to decode real neural networks.

普渡大学的研究人员正在使用人工神经网络来解码真实的神经网络。

Combined with on-policy and off-policy deep reinforcement learning algorithms, NeuEvo achieves comparable performance with artificial neural networks, as shown in the study.

结合政策内外深度强化学习算法，NeuEvo 实现了与人工神经网络相当的性能，如研究所示。

Dr Hinton popularised a clever mathematical algorithm known as backpropagation to solve this problem in artificial neural networks.

辛顿博士推广了一种称为反向传播的巧妙的数学算法来解决人工神经网络中的这个问题。

To understand how they work, imagine an artificial neural network trained to spot birds in images.

为了了解它们的工作原理，想象一下一个经过训练可以识别图像中鸟类的人工神经网络。

This is changing and neuroscience having moved on it is now known that artificial neural networks are fundamentally different from their biological counterparts, Dr.

这种情况正在发生变化，神经科学已经向前迈进，现在人们已经知道人工神经网络与它们的生物学对应物有着根本的不同，Dr.

Nobody has yet built an artificial network with 86 billion neurons, but as computing technology improves, it may be possible to keep track of such massive data sets.

尚没有人构建具有860亿个神经元的人工网络，但是随着计算技术的改进，跟踪如此大量的数据集是有可能的。

Another indication that the analogy between artificial neural networks and natural ones is useful is that the study of the former can make testable predictions about the latter.

人工神经网络与自然神经网络之间的类比有用的另一个迹象是，对前者的研究可以对后者做出可检验的预测。

His hope was that by developing artificial neural networks that could learn to solve complex problems, light might be shed on how the brain's neural networks do the same.

他希望通过开发能够学习解决复杂问题的人工神经网络，揭示大脑神经网络如何做到同样的事情。

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。

This approach has driven rapid progress in computer vision, language translation and, most recently, the human-like conversational skills of chatbots such as GPT-4. The learning is done by software models called " artificial neural networks" (ANNs).

这种方法推动了计算机视觉、语言翻译以及最近聊天机器人（如 GPT-4）的类人对话技能的快速发展。学习是通过称为“人工神经网络”（ANN）的软件模型完成的。