Sparse Distributed Memory Prototype Principles Of Operation

Sparse Distributed Memory Prototype: Principles of Operation

Sparse Distributed Memory and Related Models

Abstract: "This paper describes 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 paper compares and contrasts SDM to (1) computer memory, (2) correlation-matrix memory, (3) feed-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 associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at RIACS in 1986-1991 is highlighted."

An Alternative Design for a Sparse Distributed Memory