A bioinspired in-materia analog photoelectronic reservoir computing for human action processing

Hangyuan Cui, Yu Xiao, Yang Yang, Mengjiao Pei, Shuo Ke, Xiao Fang, Lesheng Qiao, Kailu Shi, Haotian Long, Weigao Xu, Pingqiang Cai, Peng Lin (), Yi Shi (), Qing Wan () and Changjin Wan ()
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Hangyuan Cui: Nanjing University
Yu Xiao: Zhejiang University
Yang Yang: Nanjing University
Mengjiao Pei: Nanjing University
Shuo Ke: Nanjing University
Xiao Fang: Nanjing University
Lesheng Qiao: Nanjing University
Kailu Shi: Nanjing University
Haotian Long: Nanjing University
Weigao Xu: Nanjing University
Pingqiang Cai: Nanjing University
Peng Lin: Zhejiang University
Yi Shi: Nanjing University
Qing Wan: Yongjiang Laboratory (Y-LAB)
Changjin Wan: Nanjing University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Current computer vision is data-intensive and faces bottlenecks in shrinking computational costs. Incorporating physics into a bioinspired visual system is promising to offer unprecedented energy efficiency, while the mismatch between physical dynamics and bioinspired algorithms makes the processing of real-world samples rather challenging. Here, we report a bioinspired in-materia analogue photoelectronic reservoir computing for dynamic vision processing. Such system is built based on InGaZnO photoelectronic synaptic transistors as the reservoir and a TaOX-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented, simplifying the feature extraction process. High recognition accuracies (>90%) on four motion recognition datasets are achieved based on such system. Furthermore, falling behaviors recognition is also verified by our system with low energy consumption for processing per action (~45.78 μJ) which outperforms most previous reports on human action processing. Our results are of profound potential for advancing computer vision based on neuromorphic electronics.

Date: 2025
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DOI: 10.1038/s41467-025-56899-3

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