Abstract: This paper explores internal representation power of product units  that act as the
functional nodes in the hidden layer of a multi-layer feedforward network. Interesting properties
from using binary input provide an insight into the superior computational power of the product
unit. Using binary computation problems of symmetry and parity as illustrative examples, we
show that learning arbitrary complex internal representations is more achievable with product
units than with traditional summing units.