Statistical prediction with Kanerva’s sparse distributed memory

A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over- capacity, where the associative-memory behavior of the mod-el breaks down, the processing performed by the model can be inter-preted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical view-point of sparse distributed memory and for which the standard for-mulation of SDM is a special case. This viewpoint suggests possi-ble enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with 'Genetic Algorithms', and a method for improving the capacity of SDM even when used as an associative memory. OVERVIEW This work is the result of studies involving two seemingly separate topics tha