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Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

Author

Listed:
  • Alexander Serb

    (University of Southampton)

  • Johannes Bill

    (Institute for Theoretical Computer Science, Graz University of Technology
    Heidelberg University, Kirchhoff Institute for Physics)

  • Ali Khiat

    (University of Southampton)

  • Radu Berdan

    (Imperial College)

  • Robert Legenstein

    (Institute for Theoretical Computer Science, Graz University of Technology)

  • Themis Prodromakis

    (University of Southampton)

Abstract

In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12611
    DOI: 10.1038/ncomms12611
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    Cited by:

    1. Xu, Quan & Wang, Yiteng & Wu, Huagan & Chen, Mo & Chen, Bei, 2024. "Periodic and chaotic spiking behaviors in a simplified memristive Hodgkin-Huxley circuit," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    2. Jinshi Li & Pingchuan Shen & Zeyan Zhuang & Junqi Wu & Ben Zhong Tang & Zujin Zhao, 2023. "In-situ electro-responsive through-space coupling enabling foldamers as volatile memory elements," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. 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.
    4. Lin, Yi & Liu, Wenbo & Hang, Cheng, 2023. "Revelation and experimental verification of quasi-periodic bursting, periodic bursting, periodic oscillation in third-order non-autonomous memristive FitzHugh-Nagumo neuron circuit," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    5. Romain Beaubois & Jérémy Cheslet & Tomoya Duenki & Giuseppe De Venuto & Marta Carè & Farad Khoyratee & Michela Chiappalone & Pascal Branchereau & Yoshiho Ikeuchi & Timothée Levi, 2024. "BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Pietro Belleri & Judith Pons i Tarrés & Iain McCulloch & Paul W. M. Blom & Zsolt M. Kovács-Vajna & Paschalis Gkoupidenis & Fabrizio Torricelli, 2024. "Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. 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).

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