How noise affects memory in linear recurrent networks
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 56 references that are in the map. First posted on arXiv 5 Sep 2024.
Builds on
5 of 56 references.
References · 5
- 2020Physical reservoir computing -- An introductory perspectivecited by 58
- 2019A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systemscited by 16
- 2020Memory and forecasting capacities of nonlinear recurrent networkscited by 11
- 2019Optimal short-term memory before the edge of chaos in driven random recurrent networkscited by 6
- 2023Memory of recurrent networks: Do we compute it right?cited by 6
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.