Journal article
Perovskite neural trees
Nature communications, Vol.11(1), pp.2245-2245
05/07/2020
DOI: 10.1038/s41467-020-16105-y
PMCID: PMC7206050
PMID: 32382036
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.
Details
- Title: Subtitle
- Perovskite neural trees
- Creators
- Hai-Tian Zhang - Purdue University West LafayetteTae Joon Park - Purdue University West LafayetteIvan A Zaluzhnyy - University of California San DiegoQi Wang - Purdue University West LafayetteShakti Nagnath Wadekar - Purdue University West LafayetteSukriti Manna - University of Illinois ChicagoRobert Andrawis - Purdue University West LafayettePeter O Sprau - University of California San DiegoYifei Sun - Purdue University West LafayetteZhen Zhang - Purdue University West LafayetteChengzi Huang - Purdue University West LafayetteHua Zhou - Argonne National LaboratoryZhan Zhang - Argonne National LaboratoryBadri Narayanan - University of LouisvilleGopalakrishnan Srinivasan - Purdue University West LafayetteNelson Hua - University of California San DiegoEvgeny Nazaretski - Brookhaven National LaboratoryXiaojing Huang - Brookhaven National LaboratoryHanfei Yan - Brookhaven National LaboratoryMingyuan Ge - Brookhaven National LaboratoryYong S Chu - Brookhaven National LaboratoryMathew J Cherukara - Argonne National LaboratoryMartin V Holt - Argonne National LaboratoryMuthu Krishnamurthy - University of IowaOleg G Shpyrko - University of California San DiegoSubramanian K.R.S Sankaranarayanan - University of Illinois ChicagoAlex Frano - University of California San DiegoKaushik Roy - Purdue University West LafayetteShriram Ramanathan - Purdue University West Lafayette
- Resource Type
- Journal article
- Publication Details
- Nature communications, Vol.11(1), pp.2245-2245
- DOI
- 10.1038/s41467-020-16105-y
- PMID
- 32382036
- PMCID
- PMC7206050
- NLM abbreviation
- Nat Commun
- ISSN
- 2041-1723
- eISSN
- 2041-1723
- Publisher
- Nature Publishing Group UK
- Grant note
- W911NF1920237 / ;
- Language
- English
- Date published
- 05/07/2020
- Academic Unit
- Mathematics
- Record Identifier
- 9984241039702771
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