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This research explores using human forebrain organoids as bio-hybrid computational elements to classify tactile Braille inputs, mapping sensor data to electrical stimulation patterns. By combining responses from a three-organoid ensemble, the system achieved 83% classification accuracy and demonstrated improved noise robustness compared to single units. The study establishes a foundational encoding framework for scalable, low-power bio-integrated computing architectures that leverage biological neural networks for specific sensing tasks.
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Encoding Tactile Stimuli for Braille Recognition with Organoids

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arXiv:2508.20850v3 Announce Type: replace-cross Abstract: 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.

Abstract: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.

Neural and Evolutionary Computing (cs.NE); Emerging Technologies (cs.ET); Robotics (cs.RO)

arXiv:2508.20850 [cs.NE]

(or arXiv:2508.20850v3 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2508.20850 Focus to learn more arXiv-issued DOI via DataCite

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