How noise affects memory in linear recurrent networks

JingChuan Guan; Tomoyuki Kubota; Yasuo Kuniyoshi; Kohei Nakajima

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arXiv · 2409.03187

How noise affects memory in linear recurrent networks

JingChuan Guan·Tomoyuki Kubota·Yasuo Kuniyoshi·Kohei Nakajima

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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.

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BibTeX RIS

JingChuan Guan, Tomoyuki Kubota, Yasuo Kuniyoshi, Kohei Nakajima. 2024-09-05. How noise affects memory in linear recurrent networks. https://arxiv.org/abs/2409.03187

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