Metastable states in the hierarchical Dyson model drive parallel processing in the hierarchical Hopfield network

DOI10.1088/1751-8113/48/1/015001zbMath1308.62183arXiv1407.5176OpenAlexW3105024731MaRDI QIDQ2939854

Elena Agliari, Francesco Guerra, Daniele Tantari, Andrea Galluzzi, Flavia Tavani, Adriano Barra

Publication date: 23 January 2015

Published in: Journal of Physics A: Mathematical and Theoretical (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1407.5176

zbMATH Keywords

neural networksinterpolation techniqueshierarchical models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Numerical interpolation (65D05) Neural nets applied to problems in time-dependent statistical mechanics (82C32) Memory and learning in psychology (91E40) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (4)

Hierarchical neural networks perform both serial and parallel processing ⋮ Hopfield model with planted patterns: a teacher-student self-supervised learning model ⋮ Real-space renormalization for the finite temperature statics and dynamics of the Dyson long-ranged ferromagnetic and spin-glass models ⋮ Non-convex multi-species Hopfield models

This page was built for publication: Metastable states in the hierarchical Dyson model drive parallel processing in the hierarchical Hopfield network