Capacity and information efficiency of the associative net

Abstract

Numerical calculations have been used to assess the performance of three different winners-take-all recall strategies for the associative net model of heteroassociative memory. Two strategies designed to improve recall when the net is partially connected or the input cues are noisy are shown to provide significantly greater capacity and information efficiency under these conditions. Estimates are made for the capacity of nets that are the size of the CA3 region of the rat hippocampus. These indicate that thousands of patterns can be stored, with the exact number highly dependent on pattern coding rates and the recall strategy. Analysis of nets with different types of structure shows that the capacity is much more sensitive to net size than to connectivity level. This has implications for neurobiology where there are examples of environmentally produced changes in both the size and connectivity of various brain regions.