Neural Nets: how do hidden units and sigmoids approximate nonlinear functions?

Just learning about neural nets and haven't really seen how/if the hidden units with sigmoid activation functions lead to the approximation of arbitrary nonlinear functions.

Any intuition or proof is useful, thanks!