A neural net capable of restoring continuous level library vectors from memory is considered. The vectors in the memory library are used to program the neural interconnects. Given a portion of one of the library vectors, the net extrapolates the remainder. Sufficient conditions for unique convergence are stated. An architecture for optical implementation of the network is proposed.