Institute of Information Theories and Applications FOI ITHEA
This paper presents some ideas about a new neural network architecture that can be compared to a
Fourier analysis when dealing periodic signals. Such architecture is based on sinusoidal activation functions with
an axo-axonic architecture . A biological axo-axonic connection between two neurons is defined as the weight
in a connection in given by the output of another third neuron. This idea can be implemented in the so called
Enhanced Neural Networks  in which two Multilayer Perceptrons are used; the first one will output the weights
that the second MLP uses to computed the desired output. This kind of neural network has universal
approximation properties  even with lineal activation functions.
* Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03.