Sparse Distributed Memory
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A functional style application of APL notation succinctly describes the architecture and principles of operation of one kind of connection-based computer. In the future it is expected that these machines will have thousands of processors and large arrays of dynamic connections. APL programs running on von Neumann computers now provide precise descriptions of connection-based machines which are convenient for exploring the potential of connection-based computation. Experience with radically new structures and different principles of operation for neural network problem solving can be obtained using virtual machines provided by software. Virtual machines are described by functions programmed on conventional computers.Two adaptive variants of the sparse distributed memory or SDM (Kanerva [1991]) show improved efficiency. The demonstrated superiority of Kanerva's new pattern weighting idea can be obtained by improved coding of the input patterns. This coding is done by generally defined preprocessing of features of representative binary input patterns. Transformed input patterns select addresses which pack distributed memories more efficiently.Coding is done by first computing customized weight vectors for each input pattern vector. Individual weighting of each pattern leads to more uniform utilization of the addresses and their corresponding memory connection weights. The derived pattern weights improve discrimination between pairs of similar inputs with few significant differences. This paper is to provide the APL community access to a concise symbolic description of Kanerva's weighted SDM machine. APL's rich set of computer modeling and exposition tools have the potential of markedly accelerating software and hardware development for array-base connectionist computing.