How to understand what each layer is learning in a Deep learning neural network? In a recent answer I read on Stack Exchange, I read about a possible way to understand more clearly what happens in each hidden layer of a neural network. 

Here's the excerpt-


  You should watch what makes neuron activated in each layer depending
  on the input. As you know each neuron will be activated (once the DNN
  is trained) for specific input combinations. By visualizing that you
  can get an idea about what exactly each layer has learned in term of
  high-low level features.


Source - High-level features of a neural network 

I wanted to know if there are any papers who have tried doing this (links would be really helpful). Meanwhile are there any other ways to understand what happens in each hidden layers?
I wanted to know if there are any papers who have tried doing this (links would be really helpful).


The best known paper on this topic is Visualizing and Understanding Convolutional Networks by Matthew Zeiler and Rob Fergus. But you can find many more papers and online articles that do the same.


  Meanwhile are there any other ways to understand what happens in each hidden layers?


Unfortunately, deep neural networks are like black boxes; we have little understanding of their inner workings. Visualizing the activations of the layers is probably the best way to tell what features they extract.