# A high-performance deep reservoir computer experimentally demonstrated with ion-gating reservoirs

https://mdr.nims.go.jp/datasets/1ad07945-da9e-4e80-bc38-83ff951600c0

## File

- [s44172-024-00227-y.pdf](https://mdr.nims.go.jp/filesets/0fabc8ab-363b-4a9f-a4ee-137c9ca3a74b/download) ([Detail](https://mdr.nims.go.jp/filesets/0fabc8ab-363b-4a9f-a4ee-137c9ca3a74b.md))

## Id

1ad07945-da9e-4e80-bc38-83ff951600c0

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-02T10:20:01.387250Z

## Updated at

2024-08-05T03:30:25.937665Z

## Published at

2024-08-05T03:30:26.010893Z

## Doi



## First published url

https://doi.org/10.1038/s44172-024-00227-y

## Date published

2024-06-19

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: A high-performance deep reservoir computer experimentally demonstrated with
    ion-gating reservoirs
  title_type: original
  lang: en

## Description

- description: While physical reservoir computing is a promising way to achieve low
    power consumption neuromorphic computing, its computational performance is still
    insufficient at a practical level. One promising approach to improving its performance
    is deep reservoir computing, in which the component reservoirs are multi-layered.
    However, all of the deep-reservoir schemes reported so far have been effective
    only for simulation reservoirs and limited physical reservoirs, and there have
    been no reports of nanodevice implementations. Here, as the first nanodevice implementation
    of deep-reservoir computing, we report a demonstration of deep physical reservoir
    computing with maximum of four layers using an ion gating reservoir, which is
    a small and high-performance physical reservoir. While the previously reported
    deep-reservoir scheme did not improve the performance of the ion gating reservoir,
    our deep-ion gating reservoir achieved a normalized mean squared error of 9.08×10-3
    on a second-order nonlinear autoregressive moving average task, which is the best
    performance of any physical reservoir so far reported in this task. More importantly,
    the device outperformed full simulation reservoir computing. The dramatic performance
    improvement of the ion gating reservoir with our deep-reservoir computing architecture
    paves the way for high-performance, large-scale, physical neural network devices.
  description_type: abstract
  lang: und

## Creator

- name: Daiki Nishioka
  role: author
  orcid: https://orcid.org/0000-0002-3369-7700
  organization: National Institute for Materials Science
- name: Takashi Tsuchiya
  role: author
  orcid: https://orcid.org/0000-0002-6950-6160
  organization: National Institute for Materials Science
- name: Masataka Imura
  role: author
  orcid: https://orcid.org/0000-0002-4236-9549
  organization: National Institute for Materials Science
- name: Yasuo Koide
  role: author
  orcid: https://orcid.org/0000-0001-8321-9822
  organization: National Institute for Materials Science
- name: Tohru Higuchi
  role: author
- name: Kazuya Terabe
  role: author
  orcid: https://orcid.org/0000-0003-3988-3456
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Deep reservoir computing
  schema: not_defined
- subject: Reservoir computing
  schema: not_defined
- subject: Ion-gating reservoir
  schema: not_defined
- subject: Electric double layer transistor
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Communications Engineering
  issn: '27313395'
  volume: '3'
  article_number: '81'

## Conference



## Related item



## Funding

- identifier: JPMJPR23H4
  funder_name: MEXT | Japan Science and Technology Agency
- identifier: JP22H04625
  funder_name: MEXT | Japan Society for the Promotion of Science
- identifier: JP22KJ2799
  funder_name: MEXT | Japan Society for the Promotion of Science
- funder_name: Iketani Science and Technology Foundation
- identifier: JPMXP1223NM5072
  funder_name: Ministry of Education, Culture, Sports, Science and Technology

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## Specimen



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## Fileset

- id: 0fabc8ab-363b-4a9f-a4ee-137c9ca3a74b
  filename: s44172-024-00227-y.pdf
  content_type: application/pdf
  size: 2189830
  md5: e7e27bba2d7877f9c083f0aa91de68e8

## Thumbnail

fileset_id: 0fabc8ab-363b-4a9f-a4ee-137c9ca3a74b
filename: s44172-024-00227-y.pdf