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\section{Outlook}
When trying to construct ANNs with an analytical approach, cubic spline activation
functions have shown to be an efficient tool, but the original idea, and the aim
of further research in this field, is to evolve the activation function using 
evolutionary algorithms.\\
The research of siGis in the field of ANNs has shown that the use of different
activation functions is an excellent way of improving an evolved ANN's performance.
The use of activation functions interpolated with the cubic spline method provides 
a way of evolving the activation function of each unit individually, and an unlimited
number of total functions. Furthermore, with cubic spline activation functions the 
changes to the function can be both small and drastic, depending on how many points 
are transformed to what degree. \\
When taking another look at our XOR-Problem example, it is obvious that the 
essential points (namely $N_{1} = (0,0); N_{2} = (1,1); N_{3} = (2,0)$) can be hit
by many different functions, since these points do not necessarily have to be part 
of the point set from which the function is interpolated.\\
Taking all this into account, it shall be interesting to test this kind of activation 
functions in an evolutionary process.
