# Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics

arXiv: https://arxiv.org/abs/2604.25481 · HTML: https://mlsanity.com/paper/2604.25481/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)

## Abstract (arXiv)

We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an entropy-regularized replicator equation. The coupled neuron-astrocyte dynamics admit a Lyapunov function, ensuring global convergence. At fixed points, astrocytic gains implement a softmax-normalized allocation over pattern similarity scores, yielding a mechanistic realization of self-attention as emergent routing on the gain simplex. In regimes of high memory load and interference, the model significantly improves retrieval accuracy relative to classical Hopfield dynamics and recent neuron-astrocyte baselines. These results establish a dynamical systems framework linking glial modulation, competitive resource allocation, and attention-like computation.