  Theorems from universal algebra such as that of Murski\u{i} from the 1970s
have a striking similarity to universal approximation results for neural nets
along the lines of Cybenko's from the 1980s. We consider here a discrete
analogue of the classical notion of a neural net which places these results in
a unified setting. We introduce a learning algorithm based on polymorphisms of
relational structures and show how to use it for a classical learning task.
