An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis

Junseok Lee, Seonjeong Kim, Seongjin Park, Jaesang Lee, Wonseop Hwang, Seong Won Cho, Kyuho Lee, Sun Mi Kim, Tae Yeon Seong, Cheolmin Park, Suyoun Lee, Hyunjung Yi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.

Original languageEnglish
Article number2201608
JournalAdvanced Materials
Volume34
Issue number24
DOIs
Publication statusPublished - 2022 Jun 16

Keywords

  • artificial tactile neurons
  • disease diagnosis
  • elastography
  • neuromorphic sensors
  • ovonic threshold switching
  • piezoresistive sensors
  • spiking neural networks

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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