We present a fully distributed connectionist architecture supporting lateral inhibition / winner-takes all competition. All items (individuals, relations, and structures) are represented by high-dimensional distributed vectors, and (multi)sets of items as the sum of such vectors. The architecture uses a neurally plausible permutation circuit to support a multiset intersection operation without decomposing the summed vector into its constituent items or requiring more hardware for more complex representations. Iterating this operation produces a vector in which an initially slightly favored item comes to dominate the others. This result (1) challenges the view that lateral inhibition calls for localist representation; and (2) points toward a neural implementation where more complex representations do not require more complex hardware.