Feedforward neural network structures have extensively been considered in the

literature. In a significant volume of research and development studies hyperbolic tangent

type of a neuronal nonlinearity has been utilized. This paper dwells on the widely used neuronal

activation functions as well as two new ones composed of sines and cosines, and a sinc

function characterizing the firing of a neuron. The viewpoint here is to consider the hidden

layer(s) as transforming blocks composed of nonlinear basis functions, which may assume

different forms. This paper considers 8 different activation functions which are differentiable

and utilizes Levenberg-Marquardt algorithm for parameter tuning purposes. The studies

carried out have a guiding quality based on empirical results on several training data sets.