  Associative memory or content addressable memory is an important component
function in computer science and information processing and is a key concept in
cognitive and computational brain science. Many different neural network
architectures and learning rules have been proposed to model associative memory
of the brain while investigating key functions like pattern completion and
rivalry, noise reduction, and storage capacity. A less investigated but
important function is prototype extraction where the training set comprises
pattern instances generated by distorting prototype patterns and the task of
the trained network is to recall the correct prototype pattern given a new
instance. In this paper we characterize these different aspects of associative
memory performance and benchmark six different learning rules on storage
capacity and prototype extraction. We consider only models with Hebbian
plasticity that operate on sparse distributed representations with unit
activities in the interval [0,1]. We evaluate both non-modular and modular
network architectures and compare performance when trained and tested on
different kinds of sparse random binary pattern sets, including correlated
ones. We show that covariance learning has a robust but low storage capacity
under these conditions and that the Bayesian Confidence Propagation learning
rule (BCPNN) is superior with a good margin in all cases except one, reaching a
three times higher composite score than the second best learning rule tested.
