Exploring the limits of multifunctionality across different reservoir computers-Reference-Cited by-同舟云学术

Exploring the limits of multifunctionality across different reservoir computers

Published:2022-07-18 Issue: Volume: Page:
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Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
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Author:

Flynn Andrew¹,Heilmann Oliver²,Koglmayr Daniel²,Tsachouridis Vassilios A.³,Rath Christoph⁴,Amann Andreas¹

Affiliation:

1. School of Mathematical Sciences University College Cork,Cork,Ireland

2. Ludwig-Maximilians University,Department of Physics,Munich,Germany

3. Collins Aerospace,Cork,Ireland

4. Institut für KI-Sicherheit Deutsches Zentrum für Luft-und Raumfahrt,Wessling,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892203.pdf?arnumber=9892203

Reference24 articles.

1. Short term memory in echo state networks;jaeger;GMD-German National Research Institute for Computer Science,2002

2. The ‘echo state’ approach to analysing and training recurrent neural networks-with an erratum note;jaeger;German National Research Center for Information Technology,2001

3. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

4. An experimental unification of reservoir computing methods

5. Information processing using a single dynamical node as complex system

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1. Reservoir-computing based associative memory and itinerancy for complex dynamical attractors;Nature Communications;2024-06-06

2. Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning;Scientific Reports;2024-01-04

3. Seeing double with a multifunctional reservoir computer;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-11-01

4. Multifunctionality in a Connectome-Based Reservoir Computer;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01