The re-interpretation of population codes as representing sampled probability distributions allows a set of powerful techniques from probability theory to be applied. In particular, we can access mapping theorems on probability Hilbert spaces, as well as results from Information Theory. I propose that this re-interpretation allows the development of a computational theory of population codes within which we can formulate supervised and unsupervised learning algorithms, determine optimality, and interpret neurophysiological data.