# Learning the activation function in a neural network?

Neural networks use specific activation functions, commonly used ones are tanh, ReLu.

I have seen that people have experimented with continuously parametrices activation functions, for instance here. Here, the additional parameter $$\alpha$$ which interpolates from logarithmus to linear to exponental, is optimized during the back-propagation step of the training.

I wonder whether much more general types of activation functions have been considered, such as Taylor-expansion of an unknown function where the coefficients are parameters in the training? It seems is a lot of degrees of freedom, that are not considered conventionally.

So my questions:

1. What is known about optimimality of actionation functions, apart from empirally testing? Are there mathematical proofs, for instance, that show some necessary properties for ideal learning (for instance, is monotonicity necessary, or could a sinusoidal activation function lead to optimal results too?)

2. If the answer to question 1 is no: Is there much research investigating the topic of learning activation functions?