Institute of Information Theories and Applications FOI ITHEA
Abstract:
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 [1]. 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 [2] 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 [3] even with lineal activation functions.
Description:
* Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03.