Representation and processing of structures with binary sparse distributed codes
The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures, in this paper, we provide a comparative description of sparse binary distributed representation developed in the framework of the associative-projective neural network architecture and the more well-known holographic reduced representations of Plate and binary spatter codes of Kanerva. The key procedure in associative-projective neural networks is context-dependent thinning which binds codevectors and maintains their sparseness. The codevectors are stored in structured memory array which can be realized as distributed auto-associative memory. Examples of distributed representation of structured data are given. Fast estimation of the similarity of analogical episodes by the overlap of their codevectors is used in the modeling of analogical reasoning both for retrieval of analogs from memory and for analogical mapping.
compositional distributed representations binding representation of structure analogy analogical mapping sparse coding binary coding hierarchical representation nested representation long-term memory connectionist symbol processing analogical retrieval