Add to ORKGDescribed here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associ...
Abstract—The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human l...
Challenging AI applications, such as cognitive architectures, natural language understanding, and vi...
If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, uti...
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massivel...
To determine the relation of the sparse, distributed memory to other architectures, a broad review o...
Three papers are presented on the following topics: (1) a cerebellar-model associative memory as a g...
Two artificial neural network models are compared. They are the Hopfield Neural Network Model and th...
The Sparse Distributed Memory (SDM)[1] was originally developed to tackle the problem of storing lar...
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presente...
Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could sto...
A general class of designs for a space distributed memory (SDM) is described. The author shows that ...
A new design for a Sparse Distributed Memory, called the selected-coordinate design, is described. A...
The Sparsely Distributed Memory (SDM) developed by Kanerva is an unconventional memory design with v...
The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural netwo...
For a number of years, artificial neural networks have been used for a variety of applications to au...
Abstract—The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human l...
Challenging AI applications, such as cognitive architectures, natural language understanding, and vi...
If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, uti...
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massivel...
To determine the relation of the sparse, distributed memory to other architectures, a broad review o...
Three papers are presented on the following topics: (1) a cerebellar-model associative memory as a g...
Two artificial neural network models are compared. They are the Hopfield Neural Network Model and th...
The Sparse Distributed Memory (SDM)[1] was originally developed to tackle the problem of storing lar...
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presente...
Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could sto...
A general class of designs for a space distributed memory (SDM) is described. The author shows that ...
A new design for a Sparse Distributed Memory, called the selected-coordinate design, is described. A...
The Sparsely Distributed Memory (SDM) developed by Kanerva is an unconventional memory design with v...
The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural netwo...
For a number of years, artificial neural networks have been used for a variety of applications to au...
Abstract—The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human l...
Challenging AI applications, such as cognitive architectures, natural language understanding, and vi...
If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, uti...