How noise affects memory in linear recurrent networks
<|reference_start|>How noise affects memory in linear recurrent networks: 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.<|reference_end|>
@article{guan2024how,
    title={How noise affects memory in linear recurrent networks},
    author={JingChuan Guan, Tomoyuki Kubota, Yasuo Kuniyoshi, and Kohei Nakajima},
    journal={arXiv preprint arXiv:2409.03187},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03187},
    primaryClass={cs.NE cond-mat.dis-nn cs.LG math.DS}
}