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. DOI: 10.1103/PhysRevLett.109.268101
Multitasking Associative Networks / Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Francesco, Moauro. - In: PHYSICAL REVIEW LETTERS. - ISSN 0031-9007. - ELETTRONICO. - 109:26(2012), p. 268101. [10.1103/physrevlett.109.268101]
Multitasking Associative Networks
AGLIARI, ELENA;BARRA, ADRIANO;GALLUZZI, ANDREA;GUERRA, Francesco;
2012
Abstract
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. DOI: 10.1103/PhysRevLett.109.268101I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
