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Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network. / Surazhevsky, I. A.; Demin, V. A.; Ilyasov, A. I.; Emelyanov, A. V.; Nikiruy, K. E.; Rylkov, V. V.; Shchanikov, S. A.; Bordanov, I. A.; Gerasimova, S. A.; Guseinov, D. V.; Malekhonova, N. V.; Pavlov, D. A.; Belov, A. I.; Mikhaylov, A. N.; Kazantsev, V. B.; Valenti, D.; Spagnolo, B.; Kovalchuk, M. V.

In: Chaos, Solitons and Fractals, Vol. 146, 110890, 01.05.2021.

Research output: Contribution to journal › Article › peer-review

Harvard

Surazhevsky, IA, Demin, VA, Ilyasov, AI, Emelyanov, AV, Nikiruy, KE, Rylkov, VV, Shchanikov, SA, Bordanov, IA, Gerasimova, SA, Guseinov, DV, Malekhonova, NV, Pavlov, DA, Belov, AI, Mikhaylov, AN, Kazantsev, VB, Valenti, D, Spagnolo, B & Kovalchuk, MV 2021, 'Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network', Chaos, Solitons and Fractals, vol. 146, 110890. https://doi.org/10.1016/j.chaos.2021.110890

APA

Surazhevsky, I. A., Demin, V. A., Ilyasov, A. I., Emelyanov, A. V., Nikiruy, K. E., Rylkov, V. V., Shchanikov, S. A., Bordanov, I. A., Gerasimova, S. A., Guseinov, D. V., Malekhonova, N. V., Pavlov, D. A., Belov, A. I., Mikhaylov, A. N., Kazantsev, V. B., Valenti, D., Spagnolo, B., & Kovalchuk, M. V. (2021). Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network. Chaos, Solitons and Fractals, 146, [110890]. https://doi.org/10.1016/j.chaos.2021.110890

Vancouver

Surazhevsky IA, Demin VA, Ilyasov AI, Emelyanov AV, Nikiruy KE, Rylkov VV et al. Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network. Chaos, Solitons and Fractals. 2021 May 1;146. 110890. https://doi.org/10.1016/j.chaos.2021.110890

Author

Surazhevsky, I. A. ; Demin, V. A. ; Ilyasov, A. I. ; Emelyanov, A. V. ; Nikiruy, K. E. ; Rylkov, V. V. ; Shchanikov, S. A. ; Bordanov, I. A. ; Gerasimova, S. A. ; Guseinov, D. V. ; Malekhonova, N. V. ; Pavlov, D. A. ; Belov, A. I. ; Mikhaylov, A. N. ; Kazantsev, V. B. ; Valenti, D. ; Spagnolo, B. ; Kovalchuk, M. V. / Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network. In: Chaos, Solitons and Fractals. 2021 ; Vol. 146.

BibTeX

@article{adb7c164c27942fa95b05e36892e591a,
title = "Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network",
abstract = "We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory , on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements. (C) 2021 Elsevier Ltd. All rights reserved.",
keywords = "DEPENDENT PLASTICITY, STOCHASTIC RESONANCE, NEURAL-NETWORKS, MODEL, TIME, CLASSIFICATION, TRANSPORT, SYNAPSES, LIFETIME, DEVICE",
author = "Surazhevsky, {I. A.} and Demin, {V. A.} and Ilyasov, {A. I.} and Emelyanov, {A. V.} and Nikiruy, {K. E.} and Rylkov, {V. V.} and Shchanikov, {S. A.} and Bordanov, {I. A.} and Gerasimova, {S. A.} and Guseinov, {D. V.} and Malekhonova, {N. V.} and Pavlov, {D. A.} and Belov, {A. I.} and Mikhaylov, {A. N.} and Kazantsev, {V. B.} and D. Valenti and B. Spagnolo and Kovalchuk, {M. V.}",
note = "Publisher Copyright: {\textcopyright} 2021",
year = "2021",
month = may,
day = "1",
doi = "10.1016/j.chaos.2021.110890",
language = "English",
volume = "146",
journal = "Chaos, Solitons and Fractals",
issn = "0960-0779",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network

AU - Surazhevsky, I. A.

AU - Demin, V. A.

AU - Ilyasov, A. I.

AU - Emelyanov, A. V.

AU - Nikiruy, K. E.

AU - Rylkov, V. V.

AU - Shchanikov, S. A.

AU - Bordanov, I. A.

AU - Gerasimova, S. A.

AU - Guseinov, D. V.

AU - Malekhonova, N. V.

AU - Pavlov, D. A.

AU - Belov, A. I.

AU - Mikhaylov, A. N.

AU - Kazantsev, V. B.

AU - Valenti, D.

AU - Spagnolo, B.

AU - Kovalchuk, M. V.

N1 - Publisher Copyright: © 2021

PY - 2021/5/1

Y1 - 2021/5/1

N2 - We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory , on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements. (C) 2021 Elsevier Ltd. All rights reserved.

AB - We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory , on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements. (C) 2021 Elsevier Ltd. All rights reserved.

KW - DEPENDENT PLASTICITY

KW - STOCHASTIC RESONANCE

KW - NEURAL-NETWORKS

KW - MODEL

KW - TIME

KW - CLASSIFICATION

KW - TRANSPORT

KW - SYNAPSES

KW - LIFETIME

KW - DEVICE

UR - http://www.scopus.com/inward/record.url?scp=85103380439&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/06363987-52dd-3caf-b8cb-3ecc17d6696b/

U2 - 10.1016/j.chaos.2021.110890

DO - 10.1016/j.chaos.2021.110890

M3 - Article

AN - SCOPUS:85103380439

VL - 146

JO - Chaos, Solitons and Fractals

JF - Chaos, Solitons and Fractals

SN - 0960-0779

M1 - 110890

ER -

ID: 88195801