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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

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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