Standard deviation in the # of neurons per layer In a neural network, by how much does the # of neurons typically vary from layer to layer?

PLEASE NOTE:
I am NOT asking how to find the optimal # of neurons per lyr.

As a hardware design engineer with no practical experience programming neural networks, I would like to glean for example... 


By how much does the number of neurons in hidden layers typically vary from that of the input layer?
What is the maximum deviation in the # of hidden lyr neurons to the # of input layer neurons? 
How commonly do you see a large spike in the number of neurons?


It likely depends on the application so I would like to hear from as many people as possible. Please tell me about your experience.
Input layers will always have the dimensionality of your input data(for every model I can think of).
See above, the deviation between hidden layers can be significant. For example, 128 in the first hidden and 64 in the rest(or vice versa).
This question in particular will always be problem dependent. It is decided via architecture search or intuition/experience combined with some exploratory search.