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...
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-10-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-21235-y |
Summary: | 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. |
---|---|
ISSN: | 2045-2322 |