  We propose a novel architecture to design a neural associative memory that is
capable of learning a large number of patterns and recalling them later in
presence of noise. It is based on dividing the neurons into local clusters and
parallel plains, very similar to the architecture of the visual cortex of
macaque brain. The common features of our proposed architecture with those of
spatially-coupled codes enable us to show that the performance of such networks
in eliminating noise is drastically better than the previous approaches while
maintaining the ability of learning an exponentially large number of patterns.
Previous work either failed in providing good performance during the recall
phase or in offering large pattern retrieval (storage) capacities. We also
present computational experiments that lend additional support to the
theoretical analysis.
