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First steps towards the realization of a double layer perceptron based on organic memristive devices. / Emelyanov, A. V.; Lapkin, D. A.; Demin, V. A.; Erokhin, V. V.; Battistoni, S.; Baldi, G.; Dimonte, A.; Korovin, A. N.; Iannotta, S.; Kashkarov, P. K.; Kovalchuk, M. V.

в: AIP Advances, Том 6, № 11, 111301, 01.11.2016.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Emelyanov, AV, Lapkin, DA, Demin, VA, Erokhin, VV, Battistoni, S, Baldi, G, Dimonte, A, Korovin, AN, Iannotta, S, Kashkarov, PK & Kovalchuk, MV 2016, 'First steps towards the realization of a double layer perceptron based on organic memristive devices', AIP Advances, Том. 6, № 11, 111301. https://doi.org/10.1063/1.4966257

APA

Emelyanov, A. V., Lapkin, D. A., Demin, V. A., Erokhin, V. V., Battistoni, S., Baldi, G., Dimonte, A., Korovin, A. N., Iannotta, S., Kashkarov, P. K., & Kovalchuk, M. V. (2016). First steps towards the realization of a double layer perceptron based on organic memristive devices. AIP Advances, 6(11), [111301]. https://doi.org/10.1063/1.4966257

Vancouver

Emelyanov AV, Lapkin DA, Demin VA, Erokhin VV, Battistoni S, Baldi G и пр. First steps towards the realization of a double layer perceptron based on organic memristive devices. AIP Advances. 2016 Нояб. 1;6(11). 111301. https://doi.org/10.1063/1.4966257

Author

Emelyanov, A. V. ; Lapkin, D. A. ; Demin, V. A. ; Erokhin, V. V. ; Battistoni, S. ; Baldi, G. ; Dimonte, A. ; Korovin, A. N. ; Iannotta, S. ; Kashkarov, P. K. ; Kovalchuk, M. V. / First steps towards the realization of a double layer perceptron based on organic memristive devices. в: AIP Advances. 2016 ; Том 6, № 11.

BibTeX

@article{a350c34af0a24a789bf76eabd4943c24,
title = "First steps towards the realization of a double layer perceptron based on organic memristive devices",
abstract = "Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.",
author = "Emelyanov, {A. V.} and Lapkin, {D. A.} and Demin, {V. A.} and Erokhin, {V. V.} and S. Battistoni and G. Baldi and A. Dimonte and Korovin, {A. N.} and S. Iannotta and Kashkarov, {P. K.} and Kovalchuk, {M. V.}",
note = "Publisher Copyright: {\textcopyright} 2016 Author(s).",
year = "2016",
month = nov,
day = "1",
doi = "10.1063/1.4966257",
language = "English",
volume = "6",
journal = "AIP Advances",
issn = "2158-3226",
publisher = "American Institute of Physics",
number = "11",

}

RIS

TY - JOUR

T1 - First steps towards the realization of a double layer perceptron based on organic memristive devices

AU - Emelyanov, A. V.

AU - Lapkin, D. A.

AU - Demin, V. A.

AU - Erokhin, V. V.

AU - Battistoni, S.

AU - Baldi, G.

AU - Dimonte, A.

AU - Korovin, A. N.

AU - Iannotta, S.

AU - Kashkarov, P. K.

AU - Kovalchuk, M. V.

N1 - Publisher Copyright: © 2016 Author(s).

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

AB - Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

UR - http://www.scopus.com/inward/record.url?scp=84994027721&partnerID=8YFLogxK

U2 - 10.1063/1.4966257

DO - 10.1063/1.4966257

M3 - Article

AN - SCOPUS:84994027721

VL - 6

JO - AIP Advances

JF - AIP Advances

SN - 2158-3226

IS - 11

M1 - 111301

ER -

ID: 88203096