Frontiers in Artificial Intelligence· 2026Q1
Encoding tactile stimuli for Braille recognition with organoids
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- Q1SCImago
- 2026year
Short summary
Neural organoids classified Braille letters with 61% accuracy using a transferable encoding strategy mapping tactile sensor data to electrical stimulation patterns, improving to 83% with a three-organoid ensemble.
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Key points
- A transferable encoding strategy maps tactile sensor data to electrical stimulation patterns for neural organoids.
- A single organoid achieved 61% accuracy in classifying Braille letters.
- A three-organoid ensemble improved classification accuracy to 83% and showed increased robustness to noise.
- Organoids were characterized by their electrical stimulation responses (spike activity, center of activity displacement).
AI-generated from the title and abstract; the full text is not read.
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 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.
The authors' abstract, as published at the source. Frontiers in Artificial Intelligence, 2026 · DOI ↗
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Field: Cellular and Molecular Neuroscience
Cellular and Molecular NeuroscienceNeuroscience