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Signal Processing by a Reservoir Network on Memristors. / Tarkov, Mikhail S.; Jing, Ma.

Advances in Neural Computation, Machine Learning, and Cognitive Research IX. ред. / Boris Kryzhanovsky; Witali Dunin-Barkowski; Vladimir Redko; Yury Tiumentsev; Valentin V. Klimov. Springer, 2026. стр. 3-12 1 (Studies in Computational Intelligence; Том 1241 SCI).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Tarkov, MS & Jing, M 2026, Signal Processing by a Reservoir Network on Memristors. в B Kryzhanovsky, W Dunin-Barkowski, V Redko, Y Tiumentsev & VV Klimov (ред.), Advances in Neural Computation, Machine Learning, and Cognitive Research IX., 1, Studies in Computational Intelligence, Том. 1241 SCI, Springer, стр. 3-12, XXVII International Conference on Neuroinformatics, Москва, Российская Федерация, 20.10.2025. https://doi.org/10.1007/978-3-032-07690-8_1

APA

Tarkov, M. S., & Jing, M. (2026). Signal Processing by a Reservoir Network on Memristors. в B. Kryzhanovsky, W. Dunin-Barkowski, V. Redko, Y. Tiumentsev, & V. V. Klimov (Ред.), Advances in Neural Computation, Machine Learning, and Cognitive Research IX (стр. 3-12). [1] (Studies in Computational Intelligence; Том 1241 SCI). Springer. https://doi.org/10.1007/978-3-032-07690-8_1

Vancouver

Tarkov MS, Jing M. Signal Processing by a Reservoir Network on Memristors. в Kryzhanovsky B, Dunin-Barkowski W, Redko V, Tiumentsev Y, Klimov VV, Редакторы, Advances in Neural Computation, Machine Learning, and Cognitive Research IX. Springer. 2026. стр. 3-12. 1. (Studies in Computational Intelligence). doi: 10.1007/978-3-032-07690-8_1

Author

Tarkov, Mikhail S. ; Jing, Ma. / Signal Processing by a Reservoir Network on Memristors. Advances in Neural Computation, Machine Learning, and Cognitive Research IX. Редактор / Boris Kryzhanovsky ; Witali Dunin-Barkowski ; Vladimir Redko ; Yury Tiumentsev ; Valentin V. Klimov. Springer, 2026. стр. 3-12 (Studies in Computational Intelligence).

BibTeX

@inproceedings{caa80a78c9d147548a3f502c4a2d92f5,
title = "Signal Processing by a Reservoir Network on Memristors",
abstract = "A reservoir computing system with a memristor reservoir is simulated. The efficiency of the memristor reservoir system is compared with the classical ESN (echo state network) and DeepESN in solving the classification problem. The classification accuracy of a system consisting of only a few dozen memristors can exceed the classification accuracy of ESN and DeepESN with thousands of nodes. As experimental data show, networks with random polarity of voltages on memristors are usually better than networks with uniform polarity of voltages due to their ability to generate richer nonlinear dynamics. In general, for the same number of memristors, a smaller number of network nodes improves the nonlinear mapping ability of the system by enhancing the interaction between memristors. The optimal number of network nodes should correspond to the number of memristors and should be determined experimentally.",
keywords = "DeepESN, ESN (echo state network), memristor reservoir",
author = "Tarkov, {Mikhail S.} and Ma Jing",
note = "Tarkov, M.S., Jing, M. (2026). Signal Processing by a Reservoir Network on Memristors. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y., Klimov, V.V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IX. NEUROINFORMATICS 2025. Studies in Computational Intelligence, vol 1241. Springer, Cham. https://doi.org/10.1007/978-3-032-07690-8_1; XXVII International Conference on Neuroinformatics ; Conference date: 20-10-2025 Through 24-10-2025",
year = "2026",
doi = "10.1007/978-3-032-07690-8_1",
language = "English",
isbn = "978-3-032-07689-2",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "3--12",
editor = "Boris Kryzhanovsky and Witali Dunin-Barkowski and Vladimir Redko and Yury Tiumentsev and Klimov, {Valentin V.}",
booktitle = "Advances in Neural Computation, Machine Learning, and Cognitive Research IX",
address = "United States",

}

RIS

TY - GEN

T1 - Signal Processing by a Reservoir Network on Memristors

AU - Tarkov, Mikhail S.

AU - Jing, Ma

N1 - Conference code: 27

PY - 2026

Y1 - 2026

N2 - A reservoir computing system with a memristor reservoir is simulated. The efficiency of the memristor reservoir system is compared with the classical ESN (echo state network) and DeepESN in solving the classification problem. The classification accuracy of a system consisting of only a few dozen memristors can exceed the classification accuracy of ESN and DeepESN with thousands of nodes. As experimental data show, networks with random polarity of voltages on memristors are usually better than networks with uniform polarity of voltages due to their ability to generate richer nonlinear dynamics. In general, for the same number of memristors, a smaller number of network nodes improves the nonlinear mapping ability of the system by enhancing the interaction between memristors. The optimal number of network nodes should correspond to the number of memristors and should be determined experimentally.

AB - A reservoir computing system with a memristor reservoir is simulated. The efficiency of the memristor reservoir system is compared with the classical ESN (echo state network) and DeepESN in solving the classification problem. The classification accuracy of a system consisting of only a few dozen memristors can exceed the classification accuracy of ESN and DeepESN with thousands of nodes. As experimental data show, networks with random polarity of voltages on memristors are usually better than networks with uniform polarity of voltages due to their ability to generate richer nonlinear dynamics. In general, for the same number of memristors, a smaller number of network nodes improves the nonlinear mapping ability of the system by enhancing the interaction between memristors. The optimal number of network nodes should correspond to the number of memristors and should be determined experimentally.

KW - DeepESN

KW - ESN (echo state network)

KW - memristor reservoir

UR - https://www.scopus.com/pages/publications/105020095575

UR - https://www.mendeley.com/catalogue/d98cc7ab-af90-37b2-9444-187cb4b53d59/

U2 - 10.1007/978-3-032-07690-8_1

DO - 10.1007/978-3-032-07690-8_1

M3 - Conference contribution

SN - 978-3-032-07689-2

T3 - Studies in Computational Intelligence

SP - 3

EP - 12

BT - Advances in Neural Computation, Machine Learning, and Cognitive Research IX

A2 - Kryzhanovsky, Boris

A2 - Dunin-Barkowski, Witali

A2 - Redko, Vladimir

A2 - Tiumentsev, Yury

A2 - Klimov, Valentin V.

PB - Springer

T2 - XXVII International Conference on Neuroinformatics

Y2 - 20 October 2025 through 24 October 2025

ER -

ID: 71986700