A hybrid bioelectronic retina-probe interface for object recognition.

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.