  We introduce a bipartite, diluted and frustrated, network as a sparse
restricted Boltzman machine and we show its thermodynamical equivalence to an
associative working memory able to retrieve multiple patterns in parallel
without falling into spurious states typical of classical neural networks. We
focus on systems processing in parallel a finite (up to logarithmic growth in
the volume) amount of patterns, mirroring the low-level storage of standard
Amit-Gutfreund-Sompolinsky theory. Results obtained trough statistical
mechanics, signal-to-noise technique and Monte Carlo simulations are overall in
perfect agreement and carry interesting biological insights. Indeed, these
associative networks pave new perspectives in the understanding of multitasking
features expressed by complex systems, e.g. neural and immune networks.
