# From Dyson to Hopfield: Processing on hierarchical networks

arXiv: https://arxiv.org/abs/1407.5019 · HTML: https://mlsanity.com/paper/1407.5019/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2014.

## Built on by (most cited first, 5 of 5)

- [Hopfield model with planted patterns: a teacher-student self-supervised learning model](https://mlsanity.com/paper/2304.13710/) (2023)
- [Parallel Learning by Multitasking Neural Networks](https://mlsanity.com/paper/2308.04106/) (2023)
- [The effect of priors on Learning with Restricted Boltzmann Machines](https://mlsanity.com/paper/2412.02623/) (2024)
- [Modeling Structured Data Learning with Restricted Boltzmann Machines in the Teacher-Student Setting](https://mlsanity.com/paper/2410.16150/) (2024)
- [Statistical Mechanics of Learning via Reverberation in Bidirectional Associative Memories](https://mlsanity.com/paper/2307.08365/) (2023)

## Abstract (arXiv)

We consider statistical-mechanical models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer that their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of meta-stabilities, beyond the ordered state, which get stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform both as a serial processor as well as a parallel processor, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, graph theory, signal-to-noise technique and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.