Why use tanh for activation function of MLP?

Im personally studying theories of neural network and got some questions.

In many books and references, for activation function of hidden layer, hyper-tangent functions were used.

Books came up with really simple reason that linear combinations of tanh functions can describe nearly all shape of functions with given error.

But, there came a question.

1. Is this a real reason why tanh function is used?
2. If then, is it the only reason why tanh function is used?
3. if then, is tanh function the only function that can do that?
4. if not, what is the real reason?..

I stock here keep thinking... please help me out of this mental(?...) trap!

In truth both tanh and logistic functions can be used. The idea is that you can map any real number ( [-Inf, Inf] ) to a number between [-1 1] or [0 1] for the tanh and logistic respectively. In this way, it can be shown that a combination of such functions can approximate any non-linear function. Now regarding the preference for the tanh over the logistic function is that the first is symmetric regarding the 0 while the second is not. This makes the second one more prone to saturation of the later layers, making training more difficult.