Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves

• Published in: Nature communications 2020-03, Vol.11 (1), p.1369-1369, Article 1369
• Main Authors: Tan, Hongwei, Tao, Quanzheng, Pande, Ishan, Majumdar, Sayani, Liu, Fu, Zhou, Yifan, Persson, Per O. Å., Rosen, Johanna, van Dijken, Sebastiaan
• Format: Article
• Language: English
• Subjects: 639/166/987; 639/766/25; Action Potentials; Afferent Pathways - physiology; Analog circuits; Analog to digital conversion; Biomimetics; Braille; Coding; Digital electronics; Feature extraction; Firing pattern; Handwriting recognition; Humanities and Social Sciences; Humans; Information processing; Learning; Learning - physiology; Light emitting diodes; Mechanoreceptors; Mechanoreceptors - physiology; Morse code; Movement; multidisciplinary; Nerves; Nervous system; Neural coding; Neurons; Neurons - pathology; Object recognition; Optoelectronics; Peripheral Nervous System - physiology; Physics; Pressure; Science; Science (multidisciplinary); Sensors; Sensory integration; Sensory neurons; Skin; Skin - innervation; Spiking; Synapses; Synapses - pathology; Tactile; Tactile discrimination learning; Touch - physiology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-15105-2

The integration and cooperation of mechanoreceptors, neurons and synapses in somatosensory systems enable humans to efficiently sense and process tactile information. Inspired by biological somatosensory systems, we report an optoelectronic spiking afferent nerve with neural coding, perceptual learning and memorizing capabilities to mimic tactile sensing and processing. Our system senses pressure by MXene-based sensors, converts pressure information to light pulses by coupling light-emitting diodes to analog-to-digital circuits, then integrates light pulses using a synaptic photomemristor. With neural coding, our spiking nerve is capable of not only detecting simultaneous pressure inputs, but also recognizing Morse code, braille, and object movement. Furthermore, with dimensionality-reduced feature extraction and learning, our system can recognize and memorize handwritten alphabets and words, providing a promising approach towards e-skin, neurorobotics and human-machine interaction technologies. Designing artificial somatosensory systems to efficiently emulate biological tactile information sensing, coding, and processing remains a challenge. Here, the authors demonstrate a tactile sensory system based on optoelectronic spiking afferent nerves with both coding and learning capabilities.