BoltzCONS : dynamic symbol structures in a connectionist network

Abstract: "BoltzCONS is a connectionist model that dynamically creates and manipulates composite symbol structures. These structures are implemented using a functional analog of linked lists, but BoltzCONS employs distributed representations and associative retrieval in place of a conventional memory organization. Associative retrieval leads to some interesting properties. For example, the model can instantaneously access any uniquely-named internal node of a tree. But the point of the work is not to reimplement linked lists in some peculiar new way; it is to show how neural networks can exhibit compositionality and distal access (the ability to reference a complex structure via an abbreviated tag), two properties that distinguish symbol processing from lower-level cognitive functions such as pattern recognition.Unlike certain other neural net models, BoltzCONS represents objects as a collection of superimposed activity patterns rather than as a set of weights. It can therefore create new stuctured objects dynamically, without reliance on iterative training procedures, without rehearsal of previously learned patterns, and without resorting to grandmother cells."