Data-set values feature scaling: sigmoid vs tanh As many papers point out, for better learning curve of a NN, it is better for a data-set to be normalized in a way such that values match a Gaussian curve.

Does this process of feature normalization apply only if we use sigmoid function as squashing function? If not what deviation is best for the tanh squashing function?
Yes it applies, and no it shouldn't matter that much between the two activation functions.