IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-33476-6.html
   My bibliography  Save this article

Self-organization of an inhomogeneous memristive hardware for sequence learning

Author

Listed:
  • Melika Payvand

    (Institute for Neuroinformatics, University of Zurich and ETH Zurich)

  • Filippo Moro

    (Institute for Neuroinformatics, University of Zurich and ETH Zurich
    Université Grenoble Alpes, CEA, Leti)

  • Kumiko Nomura

    (Corporate Research & Development Center, Toshiba Corporation)

  • Thomas Dalgaty

    (Université Grenoble Alpes, CEA, Leti)

  • Elisa Vianello

    (Université Grenoble Alpes, CEA, Leti)

  • Yoshifumi Nishi

    (Corporate Research & Development Center, Toshiba Corporation)

  • Giacomo Indiveri

    (Institute for Neuroinformatics, University of Zurich and ETH Zurich)

Abstract

Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware.

Suggested Citation

  • Melika Payvand & Filippo Moro & Kumiko Nomura & Thomas Dalgaty & Elisa Vianello & Yoshifumi Nishi & Giacomo Indiveri, 2022. "Self-organization of an inhomogeneous memristive hardware for sequence learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33476-6
    DOI: 10.1038/s41467-022-33476-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-33476-6
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-33476-6?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
    ---><---

    References listed on IDEAS

    as
    1. Stefano Ambrogio & Pritish Narayanan & Hsinyu Tsai & Robert M. Shelby & Irem Boybat & Carmelo Nolfo & Severin Sidler & Massimo Giordano & Martina Bodini & Nathan C. P. Farinha & Benjamin Killeen & Chr, 2018. "Equivalent-accuracy accelerated neural-network training using analogue memory," Nature, Nature, vol. 558(7708), pages 60-67, June.
    2. 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.
    3. Guillaume Bellec & Franz Scherr & Anand Subramoney & Elias Hajek & Darjan Salaj & Robert Legenstein & Wolfgang Maass, 2020. "A solution to the learning dilemma for recurrent networks of spiking neurons," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

    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. Peng Chen & Fenghao Liu & Peng Lin & Peihong Li & Yu Xiao & Bihua Zhang & Gang Pan, 2023. "Open-loop analog programmable electrochemical memory array," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Ruibin Mao & Bo Wen & Arman Kazemi & Yahui Zhao & Ann Franchesca Laguna & Rui Lin & Ngai Wong & Michael Niemier & X. Sharon Hu & Xia Sheng & Catherine E. Graves & John Paul Strachan & Can Li, 2022. "Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Bin Gao & Ying Zhou & Qingtian Zhang & Shuanglin Zhang & Peng Yao & Yue Xi & Qi Liu & Meiran Zhao & Wenqiang Zhang & Zhengwu Liu & Xinyi Li & Jianshi Tang & He Qian & Huaqiang Wu, 2022. "Memristor-based analogue computing for brain-inspired sound localization with in situ training," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    5. Maldonado, D. & Aguilera-Pedregosa, C. & Vinuesa, G. & García, H. & Dueñas, S. & Castán, H. & Aldana, S. & González, M.B. & Moreno, E. & Jiménez-Molinos, F. & Campabadal, F. & Roldán, J.B., 2022. "An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    6. Fadi Jebali & Atreya Majumdar & Clément Turck & Kamel-Eddine Harabi & Mathieu-Coumba Faye & Eloi Muhr & Jean-Pierre Walder & Oleksandr Bilousov & Amadéo Michaud & Elisa Vianello & Tifenn Hirtzlin & Fr, 2024. "Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    7. Thomas Dalgaty & Filippo Moro & Yiğit Demirağ & Alessio Pra & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Yijun Li & Jianshi Tang & Bin Gao & Jian Yao & Anjunyi Fan & Bonan Yan & Yuchao Yang & Yue Xi & Yuankun Li & Jiaming Li & Wen Sun & Yiwei Du & Zhengwu Liu & Qingtian Zhang & Song Qiu & Qingwen Li & He, 2023. "Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    9. Filippo Moro & Emmanuel Hardy & Bruno Fain & Thomas Dalgaty & Paul Clémençon & Alessio Prà & Eduardo Esmanhotto & Niccolò Castellani & François Blard & François Gardien & Thomas Mesquida & François Ru, 2022. "Neuromorphic object localization using resistive memories and ultrasonic transducers," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    10. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    11. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    12. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    13. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    14. Xu, Ying & Jia, Ya & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir, 2017. "Synchronization between neurons coupled by memristor," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 435-442.
    15. Ushakov, Yury & Balanov, Alexander & Savel’ev, Sergey, 2021. "Role of noise in spiking dynamics of diffusive memristor driven by heating-cooling cycles," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    16. Ui Yeon Won & Quoc An Vu & Sung Bum Park & Mi Hyang Park & Van Dam Do & Hyun Jun Park & Heejun Yang & Young Hee Lee & Woo Jong Yu, 2023. "Multi-neuron connection using multi-terminal floating–gate memristor for unsupervised learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    17. Zhou, Wei & Jin, Peipei & Dong, Yujiao & Liang, Yan & Wang, Guangyi, 2023. "Memristor neurons and their coupling networks based on Edge of Chaos Kernel," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    18. Xiangjin Wu & Asir Intisar Khan & Hengyuan Lee & Chen-Feng Hsu & Huairuo Zhang & Heshan Yu & Neel Roy & Albert V. Davydov & Ichiro Takeuchi & Xinyu Bao & H.-S. Philip Wong & Eric Pop, 2024. "Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    19. Arnab Pal & Zichun Chai & Junkai Jiang & Wei Cao & Mike Davies & Vivek De & Kaustav Banerjee, 2024. "An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    20. Parshina, Liubov & Novodvorsky, Oleg & Khramova, Olga & Gusev, Dmitriy & Polyakov, Alexander & Mikhalevsky, Vladimir & Cherebilo, Elena, 2021. "Laser synthesis of non-volatile memristor structures based on tantalum oxide thin films," Chaos, Solitons & Fractals, Elsevier, vol. 142(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:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33476-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.