Perovskite neural trees

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 Zhang; Badri Narayanan; Gopalakrishnan Srinivasan; Nelson Hua; Evgeny Nazaretski; Xiaojing Huang; Hanfei Yan; Mingyuan Ge; Yong S. Chu; Mathew J. Cherukara; Martin V. Holt; Muthu Krishnamurthy; Oleg G. Shpyrko; Subramanian K.R.S. Sankaranarayanan; Alex Frano; Kaushik Roy; Shriram Ramanathan

doi:10.1038/s41467-020-16105-y

Perovskite neural trees

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 Zhang
, Badri Narayanan
, Gopalakrishnan Srinivasan
, Nelson Hua
, Evgeny Nazaretski
, Xiaojing Huang
, Hanfei Yan
, Mingyuan Ge

Yong S. Chu, Mathew J. Cherukara, Martin V. Holt, Muthu Krishnamurthy, Oleg G. Shpyrko, Subramanian K.R.S. Sankaranarayanan, Alex Frano, Kaushik Roy, Shriram Ramanathan

^*此作品的通讯作者

Purdue University
University of California at San Diego
Argonne National Laboratory
University of Illinois at Chicago
United States Department of Energy
University of Louisville
Brookhaven National Laboratory
University of Iowa

科研成果: 期刊稿件 › 文章 › 同行评审

67 引用（Scopus）

摘要

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.

源语言	英语
文章编号	2245
期刊	Nature Communications
卷	11
期	1
DOI	https://doi.org/10.1038/s41467-020-16105-y
出版状态	已出版 - 1 12月 2020
已对外发布	是

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Zhang, H. T., Park, T. J., Zaluzhnyy, I. A., Wang, Q., Wadekar, S. N., Manna, S., Andrawis, R., Sprau, P. O., Sun, Y., Zhang, Z., Huang, C., Zhou, H., Zhang, Z., Narayanan, B., Srinivasan, G., Hua, N., Nazaretski, E., Huang, X., Yan, H., ... Ramanathan, S. (2020). Perovskite neural trees. Nature Communications, 11(1), 文章 2245. https://doi.org/10.1038/s41467-020-16105-y

@article{7c8e3c226ed4450ba38218ebb09f4619,

title = "Perovskite neural trees",

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.",

author = "Zhang, \{Hai Tian\} and Park, \{Tae Joon\} and Zaluzhnyy, \{Ivan A.\} and Qi Wang and Wadekar, \{Shakti Nagnath\} and Sukriti Manna and Robert Andrawis and Sprau, \{Peter O.\} and Yifei Sun and Zhen Zhang and Chengzi Huang and Hua Zhou and Zhan Zhang and Badri Narayanan and Gopalakrishnan Srinivasan and Nelson Hua and Evgeny Nazaretski and Xiaojing Huang and Hanfei Yan and Mingyuan Ge and Chu, \{Yong S.\} and Cherukara, \{Mathew J.\} and Holt, \{Martin V.\} and Muthu Krishnamurthy and Shpyrko, \{Oleg G.\} and Sankaranarayanan, \{Subramanian K.R.S.\} and Alex Frano and Kaushik Roy and Shriram Ramanathan",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1038/s41467-020-16105-y",

language = "英语",

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Zhang, HT, Park, TJ, Zaluzhnyy, IA, Wang, Q, Wadekar, SN, Manna, S, Andrawis, R, Sprau, PO, Sun, Y, Zhang, Z, Huang, C, Zhou, H, Zhang, Z, Narayanan, B, Srinivasan, G, Hua, N, Nazaretski, E, Huang, X, Yan, H, Ge, M, Chu, YS, Cherukara, MJ, Holt, MV, Krishnamurthy, M, Shpyrko, OG, Sankaranarayanan, SKRS, Frano, A, Roy, K & Ramanathan, S 2020, 'Perovskite neural trees', Nature Communications, 卷 11, 号码 1, 2245. https://doi.org/10.1038/s41467-020-16105-y

TY - JOUR

T1 - Perovskite neural trees

AU - Zhang, Hai Tian

AU - Park, Tae Joon

AU - Zaluzhnyy, Ivan A.

AU - Wang, Qi

AU - Wadekar, Shakti Nagnath

AU - Manna, Sukriti

AU - Andrawis, Robert

AU - Sprau, Peter O.

AU - Sun, Yifei

AU - Zhang, Zhen

AU - Huang, Chengzi

AU - Zhou, Hua

AU - Zhang, Zhan

AU - Narayanan, Badri

AU - Srinivasan, Gopalakrishnan

AU - Hua, Nelson

AU - Nazaretski, Evgeny

AU - Huang, Xiaojing

AU - Yan, Hanfei

AU - Ge, Mingyuan

AU - Chu, Yong S.

AU - Cherukara, Mathew J.

AU - Holt, Martin V.

AU - Krishnamurthy, Muthu

AU - Shpyrko, Oleg G.

AU - Sankaranarayanan, Subramanian K.R.S.

AU - Frano, Alex

AU - Roy, Kaushik

AU - Ramanathan, Shriram

PY - 2020/12/1

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AB - 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.

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U2 - 10.1038/s41467-020-16105-y

DO - 10.1038/s41467-020-16105-y

M3 - 文章

C2 - 32382036

AN - SCOPUS:85084406089

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 2245

ER -

Perovskite neural trees

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