During the initial research into multi-layer neural networks, it appeared that only the input and output layers had any human-comprehendible meaning; anything else would be an indecipherable vector of how much weight each item on the previous layer should have, e.g. 0.1*pixel[0] + 0.004*pixel[2] + … 0.4*pixel[1024]

In more recent research, people noticed that sensible labels can be given by seeing what sort of inputs activated it. For example, if that neuron lights up with any picture with a lot of right-angles, and nothing else, a researcher may call it “the right-angle neuron”.

In this case, the researchers did this to the CLIP network and found it was much easier than usual for humans to come up with sensible names for the type of thing any given neuron would recognise.

You can also demonstrate what a neuron does by running the maths in reverse to get an input which would activate that neuron more strongly than any other input; in this case an image.