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. Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses.
- Authors:
-
[1];
[2];
[4];
[4];
[6];
more »
- Purdue Univ., West Lafayette, IN (United States)
- Univ. of California, San Diego, CA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Illinois, Chicago, IL (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. of Louisville, KY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Univ. of Iowa, Iowa City, IA (United States)
- Publication Date:
- Research Org.:
- Energy Frontier Research Centers (EFRC) (United States). Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C); Brookhaven National Lab. (BNL), Upton, NY (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); US Air Force Office of Scientific Research (AFOSR); US Army Research Office (ARO)
- OSTI Identifier:
- 1656445
- Alternate Identifier(s):
- OSTI ID: 1660441
- Report Number(s):
- BNL-216307-2020-JAAM
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- SC0012704; FA9550-19-1-0351; W911NF1920237; SC0019273; SC0001805; AC02-06CH11357; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Zhang, Hai-Tian, Park, Tae Joon, Zaluzhnyy, Ivan A., Wang, Qi, Wadekar, Shakti Nagnath, Manna, Sukriti, Andrawis, Robert, Sprau, Peter O., Sun, Yifei, Zhang, Zhen, Huang, Chengzi, Zhou, Hua, Zhang, Zhan, Narayanan, Badri, Srinivasan, Gopalakrishnan, Hua, Nelson, Nazaretski, Evgeny, Huang, Xiaojing, Yan, Hanfei, Ge, Mingyuan, Chu, Yong S., Cherukara, Mathew J., Holt, Martin V., Krishnamurthy, Muthu, Shpyrko, Oleg G., Sankaranarayanan, Subramanian K.R.S., Frano, Alex, Roy, Kaushik, and Ramanathan, Shriram. Perovskite neural trees. United States: N. p., 2020.
Web. doi:10.1038/s41467-020-16105-y.
Zhang, Hai-Tian, Park, Tae Joon, Zaluzhnyy, Ivan A., Wang, Qi, Wadekar, Shakti Nagnath, Manna, Sukriti, Andrawis, Robert, Sprau, Peter O., Sun, Yifei, Zhang, Zhen, Huang, Chengzi, Zhou, Hua, Zhang, Zhan, Narayanan, Badri, Srinivasan, Gopalakrishnan, Hua, Nelson, Nazaretski, Evgeny, Huang, Xiaojing, Yan, Hanfei, Ge, Mingyuan, Chu, Yong S., Cherukara, Mathew J., Holt, Martin V., Krishnamurthy, Muthu, Shpyrko, Oleg G., Sankaranarayanan, Subramanian K.R.S., Frano, Alex, Roy, Kaushik, & Ramanathan, Shriram. Perovskite neural trees. United States. https://doi.org/10.1038/s41467-020-16105-y
Zhang, Hai-Tian, Park, Tae Joon, Zaluzhnyy, Ivan A., Wang, Qi, Wadekar, Shakti Nagnath, Manna, Sukriti, Andrawis, Robert, Sprau, Peter O., Sun, Yifei, Zhang, Zhen, Huang, Chengzi, Zhou, Hua, Zhang, Zhan, Narayanan, Badri, Srinivasan, Gopalakrishnan, Hua, Nelson, Nazaretski, Evgeny, Huang, Xiaojing, Yan, Hanfei, Ge, Mingyuan, Chu, Yong S., Cherukara, Mathew J., Holt, Martin V., Krishnamurthy, Muthu, Shpyrko, Oleg G., Sankaranarayanan, Subramanian K.R.S., Frano, Alex, Roy, Kaushik, and Ramanathan, Shriram. Thu .
"Perovskite neural trees". United States. https://doi.org/10.1038/s41467-020-16105-y. https://www.osti.gov/servlets/purl/1656445.
@article{osti_1656445,
title = {Perovskite neural trees},
author = {Zhang, Hai-Tian and Park, Tae Joon and Zaluzhnyy, Ivan A. and Wang, Qi and Wadekar, Shakti Nagnath and Manna, Sukriti and Andrawis, Robert and Sprau, Peter O. and Sun, Yifei and Zhang, Zhen and Huang, Chengzi and Zhou, Hua and Zhang, Zhan and Narayanan, Badri and Srinivasan, Gopalakrishnan and Hua, Nelson and Nazaretski, Evgeny and Huang, Xiaojing and Yan, Hanfei and Ge, Mingyuan and Chu, Yong S. and Cherukara, Mathew J. and Holt, Martin V. and Krishnamurthy, Muthu and Shpyrko, Oleg G. and Sankaranarayanan, Subramanian K.R.S. and Frano, Alex and Roy, Kaushik and Ramanathan, Shriram},
abstractNote = {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. Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses.},
doi = {10.1038/s41467-020-16105-y},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {Thu May 07 00:00:00 EDT 2020},
month = {Thu May 07 00:00:00 EDT 2020}
}
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