Perovskite neural trees

• Published in: Nature communications 2020-05, Vol.11 (1), p.2245-9, Article 2245
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: 639/301/1005; 639/766/1130; Artificial intelligence; Computation; Conductance; Electric pulses; Energy efficiency; Firing pattern; High speed; Humanities and Social Sciences; Interrogation; Learning; MATERIALS SCIENCE; multidisciplinary; Neural networks; Number theory; Object recognition; Pattern recognition; Perovskites; Potentiation; Protons; Resistance; Room temperature; Science; Science (multidisciplinary); Spin glasses; Synapses; Synaptic depression; Trees
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-16105-y

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.