Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning

DOI10.1162/089976601300014592zbMath0973.68204OpenAlexW2167876429WikidataQ56864429 ScholiaQ56864429MaRDI QIDQ2708447

É. M. Kussul', Dmitri Rachkovskij

Publication date: 17 April 2001

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: http://cogprints.org/1240/1/cdt-1sp.pdf

zbMATH Keywords

distributed representationsneural network implementationsthinning procedures

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (12)

Time of searching for similar binary vectors in associative memory ⋮ Randomized projective methods for the construction of binary sparse vector representations ⋮ Representing Objects, Relations, and Sequences ⋮ A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks ⋮ Some approaches to analogical mapping with structure-sensitive distributed representations ⋮ Memory Capacities for Synaptic and Structural Plasticity ⋮ A binding procedure for distributed binary data representations ⋮ The Problem of Rapid Variable Creation ⋮ Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures ⋮ Images, Frames, and Connectionist Hierarchies ⋮ Increasing the semantic storage density of sparse distributed memory ⋮ Representation and Extrapolation in Multilayer Perceptrons

Cites Work

• On associative memory
• Tensor product variable binding and the representation of symbolic structures in connectionist systems
• Neural networks and physical systems with emergent collective computational abilities.

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