Multitasking associative networks
Abstract
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.
Cite
@article{arxiv.1111.5191,
title = {Multitasking associative networks},
author = {Elena Agliari and Adriano Barra and Andrea Galluzzi and Francesco Guerra and Francesco Moauro},
journal= {arXiv preprint arXiv:1111.5191},
year = {2013}
}
Comments
to appear on Phys.Rev.Lett