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Cited by 418Advanced Memory and Neural ComputingAdvanced Sensor and Energy Harvesting MaterialsNeuroscience and Neural Engineering

An Artificial Sensory Neuron with Tactile Perceptual Learning

Open access
Wan, Changjin, Chen, Geng, Fu, Yangming, Wang, Ming, Matsuhisa, Naoji, Pan, Shaowu, Pan, Liang, Yang, Hui +3 more·2018·Advanced Materials
DOI 10.1002/adma.201801291
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Abstract

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.

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  • Artificial sensory neurons have been used to detect tactile, noxious, and mechanical stimuli.

    “Another promising neuromorphic design for communication is artificial sensory neurons, which have already been applied to measure tactile, noxious, and mechanical stimuli.[440]”

    From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [440]

    The paper’s Abstract describes an artificial sensory neuron that detects touch patterns through a pressure sensor, supporting tactile and mechanical stimuli. It does not report detecting noxious stimuli; its reference to pain being alleviated by gentle strokes is an analogy for how two tactile inputs interact, not a noxious-stimulus measurement. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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References 39 · Cited by 418

Research works printed but not linked: 38

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Authors
Wan, C., Chen, G., Fu, Y., Wang, M., Matsuhisa, N., Pan, S., Pan, L., Yang, H., Wan, Q., Zhu, L., & Chen, X.
Affiliations
  • Nanyang Technological University (SG)
  • Key Laboratory of Graphene Technologies and Applications of Zhejiang Province Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 Zhejiang P. R. China
  • Nanjing University (CN)
Corresponding author
Chen, Xiaodong
ORCID
Venue
Advanced Materials
Publisher
Wiley
Published
2018-06-07
Volume
30
Issue
30
Pages
e1801291-e1801291
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
DOI
10.1002/adma.201801291
OpenAlex
W2806904536
Semantic Scholar
463a9236fb0d587c2da24571db2c22519e5eb0a4
Cited by
418
References
39
Citations by year
2026: 16 · 2025: 43 · 2024: 55 · 2023: 63 · 2022: 57 · 2021: 61 · 2020: 64 · 2019: 50 · 2018: 9
FWCI
17.56
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Advanced Materials (publishedVersion)
  • PubMed (publishedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • Ningbo Institute of Materials Technology and Engineering, CAS Institutional Repository (submittedVersion)
Funders
  • National Research Foundation Singapore (NRF2016NRF‐NRF1001‐21)
  • Ministry of Education - Singapore (MOE2017‐T2‐2‐107)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
Keywords
resistive pressure sensors, synaptic transistors, ionic-electronic coupling, spatio-temporal pattern recognition, machine learning integration
MeSH terms
Artificial Intelligence, Sensory Receptor Cells, Robotics, Skin, Touch

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