Self-sensitizable neuromorphic device based on adaptive hydrogen gradient

Tao Zhang, Mingjie Hu, Md Zesun Ahmed Mia, Hao Zhang, Wei Mao, Katsuyuki Fukutani, Hiroyuki Matsuzaki, Lingzhi Wen, Cong Wang, Hongbo Zhao, Xuegang Chen, Yakun Yuan, Fanqi Meng, Ke Yang, Lili Zhang, Juan Wang, Aiguo Li, Weiwei Zhao, Shiming Lei, Jikun ChenPu Yu, Abhronil Sengupta, Hai Tian Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Neuromorphic computing faces long-standing challenges in handling unknown situations beyond the preset boundaries, resulting in catastrophic information loss and model failure. These predicaments arise from the existing brain-inspired hardware's inability to grasp critical information across diverse inputs, often responding passively within unalterable boundaries. Here, we report self-sensitization in perovskite neurons based on an adaptive hydrogen gradient, transcending the conventional fixed response range to autonomously capture unrecognized information. The networks with self-sensitizable neurons work well under unknown environments by reshaping the information reception range and feature salience. It can address the information loss and achieve seamless transition, processing ∼250% more structural information than traditional networks in building detection. Furthermore, the self-sensitizable convolutional network can surpass model boundaries to tackle the data drift accompanying varying inputs, improving accuracy by ∼110% in vehicle classification. The self-sensitizable neuron enables networks to autonomously cope with unforeseen environments, opening new avenues for self-guided cognitive systems.

Original languageEnglish (US)
Pages (from-to)1799-1816
Number of pages18
JournalMatter
Volume7
Issue number5
DOIs
StatePublished - May 1 2024

All Science Journal Classification (ASJC) codes

  • General Materials Science

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