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Perovskite neural trees

Author

Listed:
  • Hai-Tian Zhang

    (Purdue University
    Purdue University)

  • Tae Joon Park

    (Purdue University)

  • Ivan A. Zaluzhnyy

    (University of California, San Diego)

  • Qi Wang

    (Purdue University)

  • Shakti Nagnath Wadekar

    (Purdue University)

  • Sukriti Manna

    (Argonne National Laboratory
    University of Illinois)

  • Robert Andrawis

    (Purdue University)

  • Peter O. Sprau

    (University of California, San Diego)

  • Yifei Sun

    (Purdue University)

  • Zhen Zhang

    (Purdue University)

  • Chengzi Huang

    (Purdue University)

  • Hua Zhou

    (Argonne National Laboratory)

  • Zhan Zhang

    (Argonne National Laboratory)

  • Badri Narayanan

    (University of Louisville)

  • Gopalakrishnan Srinivasan

    (Purdue University)

  • Nelson Hua

    (University of California, San Diego)

  • Evgeny Nazaretski

    (Brookhaven National Laboratory)

  • Xiaojing Huang

    (Brookhaven National Laboratory)

  • Hanfei Yan

    (Brookhaven National Laboratory)

  • Mingyuan Ge

    (Brookhaven National Laboratory)

  • Yong S. Chu

    (Brookhaven National Laboratory)

  • Mathew J. Cherukara

    (Argonne National Laboratory)

  • Martin V. Holt

    (Argonne National Laboratory)

  • Muthu Krishnamurthy

    (University of Iowa)

  • Oleg G. Shpyrko

    (University of California, San Diego)

  • Subramanian K.R.S. Sankaranarayanan

    (Argonne National Laboratory
    University of Illinois)

  • Alex Frano

    (University of California, San Diego)

  • Kaushik Roy

    (Purdue University)

  • Shriram Ramanathan

    (Purdue University)

Abstract

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

Suggested Citation

  • Hai-Tian Zhang & Tae Joon Park & Ivan A. Zaluzhnyy & Qi Wang & Shakti Nagnath Wadekar & Sukriti Manna & Robert Andrawis & Peter O. Sprau & Yifei Sun & Zhen Zhang & Chengzi Huang & Hua Zhou & Zhan Zhan, 2020. "Perovskite neural trees," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16105-y
    DOI: 10.1038/s41467-020-16105-y
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