An iontronic reservoir for highly robust neuromorphic prosthesis

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.