We present a fully distributed connectionist architecture sup- porting 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 intersec- tion operation without decomposing the summed vector into its constituent items or requiring more hardware for more com- plex 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 in- hibition calls for localist representation; and (2) points toward a neural implementation where more complex representations do not require more complex hardware. In this article we will argue that localist representations are not necessary to support winner-takes-all competition or lateral inhibition in general. We will present a fully dis- tributed connectionist architecture supporting lateral inhibi- tion / winner-takes all behavior, in which all items (indi- viduals, relations, and structures) are represented by high- dimensional distributed vectors, and (multi)sets of items as the sum of such vectors. Unlike a localist representation, such representations are based on a fixed neural architecture that does not need to grow as new representational categories are added.
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