  In this paper, we study approximation properties of single hidden layer
neural networks with weights varying on finitely many directions and thresholds
from an open interval. We obtain a necessary and at the same time sufficient
measure theoretic condition for density of such networks in the space of
continuous functions. Further, we prove a density result for neural networks
with a specifically constructed activation function and a fixed number of
neurons.
