How noise affects memory in linear recurrent networks
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914068655243264 |
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| author | Guan, JingChuan Kubota, Tomoyuki Kuniyoshi, Yasuo Nakajima, Kohei |
| author_facet | Guan, JingChuan Kubota, Tomoyuki Kuniyoshi, Yasuo Nakajima, Kohei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How noise affects memory in linear recurrent networks Guan, JingChuan Kubota, Tomoyuki Kuniyoshi, Yasuo Nakajima, Kohei Neural and Evolutionary Computing Disordered Systems and Neural Networks Machine Learning Dynamical Systems 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. |
| title | How noise affects memory in linear recurrent networks |
| topic | Neural and Evolutionary Computing Disordered Systems and Neural Networks Machine Learning Dynamical Systems |
| url | https://arxiv.org/abs/2409.03187 |