This study proposes a transferable encoding strategy that maps tactile sensor data to electrical stimulation patterns, enabling neural organoids to perform an open-loop artificial tactile Braille classification task. Human forebrain organoids cultured on a low-density microelectrode array (MEA) are systematically stimulated to characterize the relationship between electrical stimulation parameters (number of pulse, phase amplitude, phase duration, and trigger delay) and organoid responses, measured as spike activity and spatial displacement of the center of activity. Implemented on event-based tactile inputs recorded from the Evetac sensor, our system achieved an average Braille letter classification accuracy of 61% with a single organoid, which increased significantly to 83% when responses from a three-organoid ensemble were combined. Additionally, the multi-organoid configuration demonstrated enhanced robustness against various types of artificially introduced noise. This research demonstrates the potential of organoids as low-power, adaptive bio-hybrid computational elements and provides a foundational encoding framework for future scalable bio-hybrid computing architectures.

翻译：本研究提出一种可迁移的编码策略，将触觉传感器数据映射至电刺激模式，使神经类器官能够执行开环人工触觉盲文分类任务。通过在低密度微电极阵列上培养的人前脑类器官进行系统化电刺激，量化表征了电刺激参数（脉冲数量、相位幅值、相位持续时间和触发延迟）与类器官响应之间的关联关系，其中类器官响应通过脉冲活动与活动中心的空间位移进行测量。该系统基于Evetac传感器记录的事件型触觉输入实现，单个类器官的盲文字符分类平均准确率达到61%，而三种类器官集成组合的响应融合后，准确率显著提升至83%。此外，多类器官配置展现出对多种人工引入噪声的增强鲁棒性。本研究揭示了类器官作为低功耗自适应生物混合计算元件的潜力，并为未来可扩展的生物混合计算架构提供了基础编码框架。