  This paper studies how the generalization ability of neurons can be affected
by mutual processing of different signals. This study is done on the basis of a
feedforward artificial neural network. The mutual processing of signals can
possibly be a good model of patterns in a set generalized by a neural network
and in effect may improve generalization. In this paper it is discussed that
the interference may also cause a highly random generalization. Adaptive
activation functions are discussed as a way of reducing that type of
generalization. A test of a feedforward neural network is performed that shows
the discussed random generalization.
