IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v146y2021ics0960077921002435.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

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.

Suggested Citation

  • 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, 2021. "Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
  • Handle: RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921002435
    DOI: 10.1016/j.chaos.2021.110890
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077921002435
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2021.110890?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peng Yao & Huaqiang Wu & Bin Gao & Sukru Burc Eryilmaz & Xueyao Huang & Wenqiang Zhang & Qingtian Zhang & Ning Deng & Luping Shi & H.-S. Philip Wong & He Qian, 2017. "Face classification using electronic synapses," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    2. Spagnolo, B. & Valenti, D. & Guarcello, C. & Carollo, A. & Persano Adorno, D. & Spezia, S. & Pizzolato, N. & Di Paola, B., 2015. "Noise-induced effects in nonlinear relaxation of condensed matter systems," Chaos, Solitons & Fractals, Elsevier, vol. 81(PB), pages 412-424.
    3. B. Spagnolo & A. Dubkov & N. Agudov, 2004. "Enhancement of stability in randomly switching potential with metastable state," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 40(3), pages 273-281, August.
    4. Ryu, Ji-Ho & Kim, Sungjun, 2020. "Artificial synaptic characteristics of TiO2/HfO2 memristor with self-rectifying switching for brain-inspired computing," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
    6. A. Dubkov & B. Spagnolo, 2008. "Verhulst model with Lévy white noise excitation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 65(3), pages 361-367, October.
    7. M. Prezioso & M. R. Mahmoodi & F. Merrikh Bayat & H. Nili & H. Kim & A. Vincent & D. B. Strukov, 2018. "Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    8. Bernardo Spagnolo & Davide Valenti, 2008. "Volatility Effects on the Escape Time in Financial Market Models," Papers 0810.1625, arXiv.org.
    9. Xiaojian Zhu & Qiwen Wang & Wei D. Lu, 2020. "Memristor networks for real-time neural activity analysis," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    10. E. L. Pankratov & B. Spagnolo, 2005. "Optimization of impurity profile for p-n-junction in heterostructures," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 46(1), pages 15-19, July.
    11. Alexander Serb & Johannes Bill & Ali Khiat & Radu Berdan & Robert Legenstein & Themis Prodromakis, 2016. "Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses," Nature Communications, Nature, vol. 7(1), pages 1-9, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Choi, Woo Sik & Jang, Jun Tae & Kim, Donguk & Yang, Tae Jun & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Influence of Al2O3 layer on InGaZnO memristor crossbar array for neuromorphic applications," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    2. Ryu, Hojeong & Kim, Sungjun, 2021. "Implementation of a reservoir computing system using the short-term effects of Pt/HfO2/TaOx/TiN memristors with self-rectification," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    3. Mikhaylov, A.N. & Guseinov, D.V. & Belov, A.I. & Korolev, D.S. & Shishmakova, V.A. & Koryazhkina, M.N. & Filatov, D.O. & Gorshkov, O.N. & Maldonado, D. & Alonso, F.J. & Roldán, J.B. & Krichigin, A.V. , 2021. "Stochastic resonance in a metal-oxide memristive device," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    4. Agudov, N.V. & Dubkov, A.A. & Safonov, A.V. & Krichigin, A.V. & Kharcheva, A.A. & Guseinov, D.V. & Koryazhkina, M.N. & Novikov, A.S. & Shishmakova, V.A. & Antonov, I.N. & Carollo, A. & Spagnolo, B., 2021. "Stochastic model of memristor based on the length of conductive region," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    5. Duan, Wei-Long & Lin, Ling, 2021. "Noise and delay enhanced stability in tumor-immune responses to chemotherapy system," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    6. Kim, Dahye & Kim, Sunghun & Kim, Sungjun, 2021. "Logic-in-memory application of CMOS compatible silicon nitride memristor," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    7. Du, Chuanhong & Liu, Licai & Zhang, Zhengping & Yu, Shixing, 2021. "Double memristors oscillator with hidden stacked attractors and its multi-transient and multistability analysis," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    8. Yakimov, Arkady V. & Filatov, Dmitry O. & Gorshkov, Oleg N. & Klyuev, Alexey V. & Shtraub, Nikolay I. & Kochergin, Viktor S. & Spagnolo, Bernardo, 2021. "Influence of oxygen ion elementary diffusion jumps on the electron current through the conductive filament in yttria stabilized zirconia nanometer-sized memristor," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    9. Kim, Tae-Hyeon & Kim, Sungjoon & Hong, Kyungho & Park, Jinwoo & Hwang, Yeongjin & Park, Byung-Gook & Kim, Hyungjin, 2021. "Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    10. Alsuwian, Turki & Kousar, Farhana & Rasheed, Umbreen & Imran, Muhammad & Hussain, Fayyaz & Arif Khalil, R.M. & Algadi, Hassan & Batool, Najaf & Khera, Ejaz Ahmad & Kiran, Saira & Ashiq, Muhammad Naeem, 2021. "First principles investigation of physically conductive bridge filament formation of aluminum doped perovskite materials for neuromorphic memristive applications," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    11. Yang, Jinwoong & Ryu, Hojeong & Kim, Sungjun, 2021. "Resistive and synaptic properties modulation by electroforming polarity in CMOS-compatible Cu/HfO2/Si device," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    12. Song, Yi & Xu, Wei, 2021. "Asymmetric Lévy noise changed stability in a gene transcriptional regulatory system," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    13. Setoudeh, Farbod & Dezhdar, Mohammad Matin & Najafi, M., 2022. "Nonlinear analysis and chaos synchronization of a memristive-based chaotic system using adaptive control technique in noisy environments," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    14. Yablokov, A.A. & Glushkov, E.I. & Pankratov, A.L. & Gordeeva, A.V. & Kuzmin, L.S. & Il’ichev, E.V., 2021. "Resonant response drives sensitivity of Josephson escape detector," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    15. dos Santos, Maike A.F. & Junior, Luiz Menon, 2021. "Random diffusivity models for scaled Brownian motion," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    16. Taheri, Alireza Ghomi & Setoudeh, Farbod & Tavakoli, Mohammad Bagher & Feizi, Esmaeil, 2022. "Nonlinear analysis of memcapacitor-based hyperchaotic oscillator by using adaptive multi-step differential transform method," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    17. Setoudeh, Farbod & Dousti, Massoud, 2022. "Analysis and implementation of a meminductor-based colpitts sinusoidal oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    18. Setoudeh, F. & Sedigh, A. Khaki, 2021. "Nonlinear analysis and minimum L2-norm control in memcapacitor-based hyperchaotic system via online particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    19. Mahata, Chandreswar & Kim, Sungjun, 2021. "Electrical and optical artificial synapses properties of TiN-nanoparticles incorporated HfAlO-alloy based memristor," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    20. Park, Jinwoo & Kim, Tae-Hyeon & Kim, Sungjoon & Lee, Geun Ho & Nili, Hussein & Kim, Hyungjin, 2021. "Conduction mechanism effect on physical unclonable function using Al2O3/TiOX memristors," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921002435. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.