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Abstract
The associative net is a fully connected feedforward associative memory, with one layer of input units, one layer of output units and binary valued weights. Its simple structure and the form of the weight modification rule used have led to several analyses of two measures of its performance: capacity and information efficiency. However, these have yielded only approximate expressions. In this paper we present a more precise treatment. Simulation results are presented to support the analysis and to deal with the cases where analysis is not possible. We extend previous work which showed that in some cases it may be more efficient (in information theoretic terms) to store many patterns that are each retrieved with a high error rate rather than fewer patterns which are each retrieved with high accuracy.