
\maketitle

\vspace*{2.5cm}

\begin{abstract}
Quite a piece of research has been done recently in the field of activation functions. Two of those are 
especially noteworthy:\\
Sopena, Romero and Alqu\'ezar showed that periodic activation functions (as opposed to the 
traditional monotonic functions) are much better at learning classification problems like
 XOR, Two-Quadrants or Two-Spirals.\\
On the other hand a local research group found that, if an ANN is evolved, evolving activation functions
(by using tables of supported function) and topology yields far better results than ordinary evolution 
of topology.\\
These results combined lead to the conclusion that a not necessarily monotonic fully evolvable activation
function is supperior in fitness. The use of cubic splines fits these parameters and a few more:\\
The ability to create cubic splines from tabulated functions (cubic spline interpolation) yields the 
possibility to evolve the tabulated function and removes the strain to use timeconsuming mathematical 
calculation during evolution.\\
Cubic splines have a smooth first and continuous second derivative, thus they are suitable for the standard learing
methodes.\\
This article discusses the implementation and applicability of cubic spline interpolated activation 
functions.
\end{abstract}


\newpage	
