# Robust retrieval of dynamic sequences through interaction modulation

arXiv: https://arxiv.org/abs/2211.17152 · HTML: https://mlsanity.com/paper/2211.17152/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Long Sequence Hopfield Memory](https://mlsanity.com/paper/2306.04532/) (2023)
- [Input-Driven Dynamics for Robust Memory Retrieval in Hopfield Networks](https://mlsanity.com/paper/2411.05849/) (2024)
- [A Neuromorphic Architecture for Scalable Event-Based Control](https://mlsanity.com/paper/2511.11924/) (2025)

## Abstract (arXiv)

Many biological systems dynamically rearrange their components through a sequence of configurations in order to perform their functions. Such dynamic processes have been studied using network models that sequentially retrieve a set of stored patterns. Previous models of sequential retrieval belong to a general class in which the components of the system are controlled by a feedback ("input modulation"). In contrast, we introduce a new class of models in which the feedback modifies the interactions among the components ("interaction modulation"). We show that interaction modulation models are not only capable of retrieving dynamic sequences, but they do so more robustly than input modulation models. In particular, we find that modulation of symmetric interactions allows retrieval of patterns with different activity levels and has a much larger dynamic capacity. Our results suggest that interaction modulation may be a common principle underlying biological systems that show complex collective dynamics.