Researchers at Bristol connected tiny human forebrain organoids - under 1 mm across with roughly 10,000 neurons - grown on multielectrode arrays to a tactile sensor that scanned all 26 Braille letters. Tactile recordings were converted into electrical stimulation patterns delivered to the organoids; their evoked neural activity was recorded and classified by a support vector machine. Single organoids produced reliably decodable letter signals about 61% of the time, while ensembles of three organoids reached approximately 83% accuracy. When artificial noise was added to the inputs, decoding based on organoid responses was more robust than direct machine decoding of the obscured signals.
The work shows living neural tissue can receive, transform and transmit sensor data, advancing the prospect of energy-efficient biohybrid computing that exploits brain-like networks. The team did not demonstrate learning or long-term plasticity - experiments used fixed encodings and simple activity measures - but plans to probe richer tactile features (textures, edges) and closed-loop tasks with feedback to test adaptation. Improved performance from organoid ensembles may reflect complementary variability across individual organoids, a potential advantage for information processing that also poses challenges for system standardization.
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