Multitasking associative networks.
We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzmann machine and we show its thermodynamical equivalence to an associative working memory able to retrieve several 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 through statistical mechanics, the 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.
Dipartimento di Fisica, Università degli Studi di Parma, viale Usberti 7/A, 43100 Parma, Italy.
Elena
E
Agliari
Adriano
A
Barra
Andrea
A
Galluzzi
Francesco
F
Guerra
Francesco
F
Moauro
D009415:Nerve Net; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology