The sense of touch is essential for humans to perceive, locate and react to physical stimuli. Notwithstanding the substantial advancements in e-skin research and related applications with collaborative robots and bionic prostheses, biomimetic intelligence remains a challenge in the attempt to understand and mimic somatosensory processing schemes. In this work, we present a large-area e-skin embedded with photonic fibre Bragg gratings, capable of decoding touch localization through a bioinspired two-layered spiking neuronal network. The implemented biomimicry of slowly adapting and fast-adapting type II primary afferents, cuneate neurons with overlapping receptive fields and neuroplasticity, enable unsupervised learning in localizing tactile stimuli with an error lower than 10 mm, and two-point discrimination thresholds matching human psychophysical thresholds in the forearm. These results align with biological findings and offer a promising step towards the development of bionic systems, opening new avenues for both practical applications and scientific explorations of somatosensation.
Type II mechanoreceptors and cuneate spiking neuronal network enable touch localization on a large-area e-skin
Filosa M.Secondo
Membro del Collaboration Group
;Barbosa Soares A.Penultimo
Membro del Collaboration Group
;Oddo C. M.
Ultimo
Supervision
2025-01-01
Abstract
The sense of touch is essential for humans to perceive, locate and react to physical stimuli. Notwithstanding the substantial advancements in e-skin research and related applications with collaborative robots and bionic prostheses, biomimetic intelligence remains a challenge in the attempt to understand and mimic somatosensory processing schemes. In this work, we present a large-area e-skin embedded with photonic fibre Bragg gratings, capable of decoding touch localization through a bioinspired two-layered spiking neuronal network. The implemented biomimicry of slowly adapting and fast-adapting type II primary afferents, cuneate neurons with overlapping receptive fields and neuroplasticity, enable unsupervised learning in localizing tactile stimuli with an error lower than 10 mm, and two-point discrimination thresholds matching human psychophysical thresholds in the forearm. These results align with biological findings and offer a promising step towards the development of bionic systems, opening new avenues for both practical applications and scientific explorations of somatosensation.| File | Dimensione | Formato | |
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Pereira_NatMachIntell_2025.pdf
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