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<p>Neuromorphic prosthesis demands not only the assembly of neural architectures and functions but also robustness against unpredictable failures in dynamic physiological environments. While self-healing electronics have been demonstrated to restore synapse-like functions, their application to higher-order cognitive functions remains limited. Here we present a hydrogel-based iontronic reservoir that demonstrates exceptional physical and functional robustness for neuromorphic prosthesis. The nonlinear dynamics of the hydrogel–electrode interface can serve as a physical reservoir to preprocess time series, with minimized susceptibility to physical damage. Our system based on the hydrogel-based iontronic reservoir achieves 95% accuracy in speech recognition and can restore such capability within 0.02 s after reattaching the fractured points, outperforming biological systems in the neurorehabilitation process. Moreover, its pH-sensitive dynamics enable adaptive closed-loop neural stimulation control in a rat model, validating its potential for neural rehabilitation and sensorimotor function restoration. We expect such a hydrogel-based iontronic reservoir to improve both processing efficiency and robustness for next-generation neuroprosthetics and human–machine interfaces.</p>

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An iontronic reservoir for highly robust neuromorphic prosthesis

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Mengjiao Pei,
  • Tian Gao,
  • Li Liu,
  • Wenlong Li,
  • Haotian Long,
  • Yifei Luo,
  • Zhaogang Teng,
  • Hangyuan Cui,
  • Xiang Li,
  • Qinyong Dai,
  • Kailu Shi,
  • Lesheng Qiao,
  • Baocheng Peng,
  • Qianye Xing,
  • Manhua Wen,
  • Mengtao Han,
  • Zhenhua Wan,
  • Yun Li,
  • Bin Xue,
  • Yi Cao,
  • Yi Shi,
  • Qing Wan,
  • Xiaodong Chen,
  • Changjin Wan

摘要

Neuromorphic prosthesis demands not only the assembly of neural architectures and functions but also robustness against unpredictable failures in dynamic physiological environments. While self-healing electronics have been demonstrated to restore synapse-like functions, their application to higher-order cognitive functions remains limited. Here we present a hydrogel-based iontronic reservoir that demonstrates exceptional physical and functional robustness for neuromorphic prosthesis. The nonlinear dynamics of the hydrogel–electrode interface can serve as a physical reservoir to preprocess time series, with minimized susceptibility to physical damage. Our system based on the hydrogel-based iontronic reservoir achieves 95% accuracy in speech recognition and can restore such capability within 0.02 s after reattaching the fractured points, outperforming biological systems in the neurorehabilitation process. Moreover, its pH-sensitive dynamics enable adaptive closed-loop neural stimulation control in a rat model, validating its potential for neural rehabilitation and sensorimotor function restoration. We expect such a hydrogel-based iontronic reservoir to improve both processing efficiency and robustness for next-generation neuroprosthetics and human–machine interfaces.