One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.

神经科学最深刻的见解之一是感觉编码应利用统计规律性。人类的视觉体验包含许多冗余：场景在不同时刻几乎保持不变，并且附近的图像位置通常具有相似的颜色。知道哪些规则可以塑造自然图像的视觉系统可以利用它们来紧凑地编码场景，或者猜测接下来会发生什么。尽管这些原理已被人们接受了60多年，但直到最近，才有可能仅在视觉处理的最早阶段将它们转换为显式模型。但是无监督深度学习的最新进展已经改变了这一点。可以教神经网络压缩图像或在空间或时间上做出预测。在此过程中，他们学习构成图​​像的统计规律，反过来又经常反映出外部世界中的物理对象和过程。无监督深度学习的惊人成就再次证明了学习统计规律对感觉编码的重要性，并为外界知识如何进入视觉皮层提供了一个连贯的框架。