When you train a deep convolutional neural network, the first couple of layers appear to take on this role, detecting simple features like edges and textures, which the higher layers build upon to see more complex objects.

For example https://www.researchgate.net/figure/Visualization-of-example..., where you can see (somewhat, if you zoom in) that layer 1 neurons are interested in very simple features, like strong horizontal edges, or particular gradients.