# Learning About Learning: A Path from Spin Glasses to Artificial Intelligence

arXiv: https://arxiv.org/abs/2601.07635 · HTML: https://mlsanity.com/paper/2601.07635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 50 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [A Brief History of Excitable Map-Based Neurons and Neural Networks](https://mlsanity.com/paper/1303.0256/) (2013)
- [Building a model of the brain: from detailed connectivity maps to network organization](https://mlsanity.com/paper/2106.03995/) (2021)

## Abstract (arXiv)

The Hopfield model, originally inspired by spin glasses, occupies a central place at the intersection of statistical mechanics, neural networks, and artificial intelligence. Despite its conceptual simplicity and broad applicability, it is rarely integrated into the undergraduate physics curriculum. We present the Hopfield model as a pedagogically rich framework that naturally unifies core topics from the undergraduate physics curriculum and that provides a concise introduction based on concepts such as a model's energy function, dynamics, and pattern stability. We discuss practical aspects of its simulation and provide simulation codes. We also propose problems designed to mirror research practice, which can be included in undergraduate classes.