A hybrid bioelectronic retina-probe interface for object recognition.

Journal: Biosensors & bioelectronics
Published Date: Mar 24, 2025

Abstract

Retina converts light stimuli into spike firings, encoding abundant visual information critical for both fundamental studies of the visual system and therapies for visual diseases. However, probing these spikes directly from the retina is hindered by limited recording channels, insufficient contact between the retina and electrodes, and short operational lifetimes. In this study, we developed a perforated and flexible microelectrode array to achieve a robust retina-probe interface, ensuring high-quality detection of spike firings from hundreds of neurons. Leveraging the retina's natural light-sensing ability, we created a hybrid bioelectronic system that enables image recognition through machine learning integration. We systematically explored the system's spatial resolution, and demonstrated its capability to recognize different colors and light intensities. Importantly, due to the perforated structure, the hybrid system maintained over 94 % accuracy in distinguishing light on/off conditions for 9 h ex vivo. Finally, inspired by the eye's configuration, we developed a bioelectronic mimic eye capable of recognizing objects in real environments. This work demonstrated that the hybrid bioelectronic retina-probe interface is effective not only for light sensing but also for efficient image and object recognition.

Authors

  • Yifei Ye
    2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
  • Yunxiao Lu
    2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China; College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, 201306, China.
  • Haoyang Su
    2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
  • Shuang Jin
    Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; School of Automation, Chongqing University, Chongqing 400044, China.
  • Gen Li
    Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University, School of Medicine, Shanghai, 200072, P.R.China.
  • Yingkang Yang
    2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Luyue Jiang
    2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
  • Zhitao Zhou
    State Key Lab for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing, China.
  • Xiaoling Wei
    Department of Respiratory Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Tiger H Tao
    State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. tiger@mail.sim.ac.cn.
  • Liuyang Sun
    2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. Electronic address: liuyang.sun@mail.sim.ac.cn.

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