How noise affects memory in linear recurrent networks
Neural and Evolutionary Computing
2025-10-02 v1 Disordered Systems and Neural Networks
Machine Learning
Dynamical Systems
Abstract
The effects of noise on memory in a linear recurrent network are theoretically investigated. Memory is characterized by its ability to store previous inputs in its instantaneous state of network, which receives a correlated or uncorrelated noise. Two major properties are revealed: First, the memory reduced by noise is uniquely determined by the noise's power spectral density (PSD). Second, the memory will not decrease regardless of noise intensity if the PSD is in a certain class of distribution (including power law). The results are verified using the human brain signals, showing good agreement.
Keywords
Cite
@article{arxiv.2409.03187,
title = {How noise affects memory in linear recurrent networks},
author = {JingChuan Guan and Tomoyuki Kubota and Yasuo Kuniyoshi and Kohei Nakajima},
journal= {arXiv preprint arXiv:2409.03187},
year = {2025}
}