What can we understand from max-activation generated images? There are several approaches to generate psychedelic images, providing maximum activations for individual neirons in convolutional neural networks. For example there is a lot of them there https://app.slack.com/client/T040HKJE3/threads/thread/C04655480-1581762147.250800?cdn_fallback=2 or a bit in https://arxiv.org/abs/1311.2901 (M. Zeiler) or in https://www.youtube.com/watch?v=ghEmQSxT6tw But what can we get from them?

E.g. here 
does presence of 6 fish’es pieces means there are mainly 6 different positions of fishes which the net saw on 1000 train images? Or this is rather caused by some corner effects or patter size? Like 224 source pixels/100 = 2.2 fishes horizontally.

I also don't understand well how they are obtained, so can't conjecture what aspects they can depict.

There is approach when we find parts of train/validation images which gives strongest activations. This is perfectly understandable for me.
Those are the activation maps of the learned features. 

In the case of this specific model, the filters learn that the "fishy" parts of the image:


Head-ish and dorsal fin-ish sections that define convex hulls
Scale-ish textures insides the convex hulls and surrounded by water-ish textures outside
Orange-ish and gray-ish colors insides the convex hulls and blue-ish colors outside
The head-ish section tend not to be in the corners of an image (since people frame pictures and people think faces are interesting)