Guiding principle of reservoir computing based on “small-world” network
Abstract Reservoir computing is a computational framework of recurrent neural networks and is gaining attentions because of its drastically simplified training process. For a given task to solve, however, the methodology has not yet been established how to construct an optimal reservoir. While, “sma...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21235-y |
| _version_ | 1811226622159749120 |
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| author | Ken-ichi Kitayama |
| author_facet | Ken-ichi Kitayama |
| author_sort | Ken-ichi Kitayama |
| collection | DOAJ |
| description | Abstract Reservoir computing is a computational framework of recurrent neural networks and is gaining attentions because of its drastically simplified training process. For a given task to solve, however, the methodology has not yet been established how to construct an optimal reservoir. While, “small-world” network has been known to represent networks in real-world such as biological systems and social community. This network is categorized amongst those that are completely regular and totally disordered, and it is characterized by highly-clustered nodes with a short path length. This study aims at providing a guiding principle of systematic synthesis of desired reservoirs by taking advantage of controllable parameters of the small-world network. We will validate the methodology using two different types of benchmark tests—classification task and prediction task. |
| first_indexed | 2024-04-12T09:28:13Z |
| format | Article |
| id | doaj.art-a4ecab8b6a2647fdbbede6e39229b3dc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T09:28:13Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a4ecab8b6a2647fdbbede6e39229b3dc2022-12-22T03:38:26ZengNature PortfolioScientific Reports2045-23222022-10-0112111010.1038/s41598-022-21235-yGuiding principle of reservoir computing based on “small-world” networkKen-ichi Kitayama0National Institute of Information and Communications TechnologyAbstract Reservoir computing is a computational framework of recurrent neural networks and is gaining attentions because of its drastically simplified training process. For a given task to solve, however, the methodology has not yet been established how to construct an optimal reservoir. While, “small-world” network has been known to represent networks in real-world such as biological systems and social community. This network is categorized amongst those that are completely regular and totally disordered, and it is characterized by highly-clustered nodes with a short path length. This study aims at providing a guiding principle of systematic synthesis of desired reservoirs by taking advantage of controllable parameters of the small-world network. We will validate the methodology using two different types of benchmark tests—classification task and prediction task.https://doi.org/10.1038/s41598-022-21235-y |
| spellingShingle | Ken-ichi Kitayama Guiding principle of reservoir computing based on “small-world” network Scientific Reports |
| title | Guiding principle of reservoir computing based on “small-world” network |
| title_full | Guiding principle of reservoir computing based on “small-world” network |
| title_fullStr | Guiding principle of reservoir computing based on “small-world” network |
| title_full_unstemmed | Guiding principle of reservoir computing based on “small-world” network |
| title_short | Guiding principle of reservoir computing based on “small-world” network |
| title_sort | guiding principle of reservoir computing based on small world network |
| url | https://doi.org/10.1038/s41598-022-21235-y |
| work_keys_str_mv | AT kenichikitayama guidingprincipleofreservoircomputingbasedonsmallworldnetwork |