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An Artificial Sensory Neuron with Tactile Perceptual Learning
Author(s) -
Wan Changjin,
Chen Geng,
Fu Yangming,
Wang Ming,
Matsuhisa Naoji,
Pan Shaowu,
Pan Liang,
Yang Hui,
Wan Qing,
Zhu Liqiang,
Chen Xiaodong
Publication year - 2018
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201801291
Subject(s) - sensory system , neuromorphic engineering , artificial neuron , perception , materials science , artificial intelligence , tactile sensor , computer science , interface (matter) , process (computing) , artificial neural network , human–computer interaction , robot , neuroscience , biology , capillary number , capillary action , composite material , operating system
Sensory neurons within skin form an interface between the external physical reality and the inner tactile perception. This interface enables sensory information to be organized identified, and interpreted through perceptual learning—the process whereby the sensing abilities improve through experience. Here, an artificial sensory neuron that can integrate and differentiate the spatiotemporal features of touched patterns for recognition is shown. The system comprises sensing, transmitting, and processing components that are parallel to those found in a sensory neuron. A resistive pressure sensor converts pressure stimuli into electric signals, which are transmitted to a synaptic transistor through interfacial ionic/electronic coupling via a soft ionic conductor. Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.