We describe a representation for frame-like concept structures ina neural network called DUCS. Slot names and slot fillers axe diffuse patternsof activation spread over a collection of units. Our choice of a distributedrepresentation gives rise to certain useful properties not shaied by conventionalframe systems. One of these is the ability to encode fine semantic distinctions assubtle vairiations on the canonical pattern for a slot. D U C S typically maintainsseveral concepts simultaneously in its concept memory; it can retrieve a conceptgiven one or more slots as cues. W e show how Hinton's notion of a "reduceddescription" cam be used to make one concept fill a slot in another
