@inproceedings{
herron2023modulating,
title={Modulating interactions to control dynamics of neural networks},
author={Lukas Herron and Pablo Sartori and BingKan Xue},
booktitle={Associative Memory {\&} Hopfield Networks in 2023},
year={2023},
url={https://openreview.net/forum?id=SiTNMzCwQ4}
}
Sequential retrieval of stored patterns is a fundamental task that can be performed by neural networks. Previous models of sequential retrieval belong to a general class in which the components of the network are controlled by a slow feedback ("input modulation"). In contrast, we introduce a new class of models in which the feedback modifies the interactions among the components ("interaction modulation"). In particular, we study a model in which the symmetric interactions are modulated. We show that this model is not only capable of retrieving dynamic sequences, but it does so more robustly than a canonical model of input modulation. Our model allows retrieval of patterns with different activity levels, is robust to feedback noise, and has a large dynamic capacity. Our results suggest that interaction modulation may be a new paradigm for controlling network dynamics.
Modulating interactions to control dynamics of neural networks
Lukas Herron Pablo Sartori BingKan Xue