BoltzCONS: Dynamic Symbol Structures in a Connectionist Network
Abstract
BoltzCONS is connectionist model that dynamically creates and manipulates composite symbol 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 structured objects as dynamically, without reliance on iterative training procedures, without rehearsal of previously learned patterns, and without resorting to grandmother cells. (kr)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1989
- Accession Number
- ADA225721
Entities
People
- David S. Touretzky
Organizations
- Carnegie Mellon University