Robust retrieval of dynamic sequences through interaction modulation

TL;DR

This paper introduces a novel class of models called interaction modulation for dynamic sequence retrieval, demonstrating enhanced robustness and capacity over traditional input modulation models, with implications for understanding biological systems.

Contribution

The paper proposes interaction modulation as a new mechanism for dynamic sequence retrieval, outperforming input modulation in robustness and capacity.

Findings

Interaction modulation models retrieve sequences more robustly.

Interaction modulation allows for larger dynamic capacity.

Symmetric interaction modulation enables diverse activity patterns.

Abstract

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.