How are per-layer-detected-patterns in a trained CNN plotted? In the case my question is not clear, I am talking about the patterns that are detected in each of the layers of an image-trained Convolutional Neural Network (CNN). Take the following image as an example (by Honglak Lee). I think I understand the concept: different layers start coding for different features with increasing complexity. The first layers code for edges, the middle layers code for simple features (e.g. nose, eyes), and the later layers code for whole faces.
However, I do not see the equivalence betweence each of the patterns in the picture and the network elements. Do each pattern inside a pattern-class correspond to one neuron of that layer? How are these cool pattern detection pictures even plotted?
The image you posted is derived from figures which depict the basis functions of a Convolutional Restricted Boltzmann Machine.

I found them in this ICML paper (behind a paywall, sorry) and this PhD thesis, both are by Honglak Lee, who was advised by Andrew Ng.

Strictly speaking they are not visualizations of a feed-forward Convolutional Neural Network, which might be why you are having a hard time interpreting what they depict. I don't really know much about RBM's myself so I'm afraid I can't help with their interpretation.

As far as visualizing CNN's, there are several common methods. You can visualize filter weights directly, find input images which maximally activate a filter, plot activation as a function of image occlusion, etc.
This page has a good summary of techniques, also this paper may be useful.