An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis (Adv. Mater. 24/2022). Issue 24 (16th June 2022)

• Record Type: Journal Article
• Authors: Lee, Junseok; Kim, Seonjeong; Park, Seongjin; Lee, Jaesang; Hwang, Wonseop; Cho, Seong Won; Lee, Kyuho; Kim, Sun Mi; Seong, Tae‐Yeon; Park, Cheolmin; Lee, Suyoun; Yi, Hyunjung
• Is Part Of: Advanced materials. Volume 34:Issue 24(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-16
• Subjects: artificial tactile neurons -- disease diagnosis -- elastography -- neuromorphic sensors -- ovonic threshold switching -- piezoresistive sensors -- spiking neural networks; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202270182
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract : Artificial Neurons An artificial tactile neuron based on an ovonic threshold switch and a piezoresistive sensor reported by Suyoun Lee, Hyunjung Yi, and co‐workers in article number 2201608 can encode the elastic stiffness of pressed materials into spike‐frequency evolution patterns. The spiking representation of stiffness in combination with spiking‐neural‐network‐based learning can be used to classify the malignant status of breast tumors with low energy consumption, low latency, and yet high accuracy.