The sparse distributed memory (SDM) was originally developed to tackle the problem of storing large binary data patterns. The model succeeded well in storing random input data. However, its efficiency, particularly in handling nonrandom data, was poor. In its original form it is a static and inflexible system. Most of the recent work on the SDM has concentrated on improving the efficiency of a modified form of the SDM which treats the memory as a single-layer neural network. This paper introduces an alternative SDM, the SDM signal model which retains the essential characteristics of the original SDM, while providing the memory with a greater scope for plasticity and self-evolution. By removing many of the problematic features of the original SDM the new model is not as dependent upon a priori input values. This gives it an increased robustness to learn either random or correlated input patterns. The improvements in this new SDM signal model should be also of benefit to modified SDM neural network models.