Hybrid distributed/local connectionist architectures

Summary form only given, as follows. A class of neural network architectures is described that uses both distributed and local representation. The distributed representations are used for input and output, thereby enabling associative, noise-tolerant interaction with the environment. Internally, all representations are fully local. This simplifies weight assignment and makes the networks easy to configure for specific applications. These hybrid distributed/local architectures are especially useful for applications where structured information needs to be represented. Three such applications are briefly discussed: a scheme for knowledge representation, a connectionist rule-based system, and a knowledge-base browser.<>