DataVXAdvanced MaterialsPaper
journal article·2022-06

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

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

Advanced Materials· Volume 34· Issue 24
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Source 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.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials

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Affiliations

Post‐Silicon Semiconductor Institute Korea Institute of Science and Technology Seoul 02792 Republic of Korea

YU‐KIST Yonsei University Seoul 03722 Republic of Korea

Department of Materials Science and Engineering Yonsei University Seoul 03722 Republic of Korea

Center for Neuromorphic Engineering Korea Institute of Science and Technology Seoul 02792 Republic of Korea

Department of Materials Science and Engineering Korea University Seoul 02841 Republic of Korea

Department of Materials Science and Engineering Seoul National University Seoul 08826 Republic of Korea

Department of Radiology Seoul National University Bundang Hospital Seoul National University College of Medicine Seongnam 13620 Republic of Korea

Division of Nano & Information Technology Korea University of Science and Technology Daejeon 34316 Republic of Korea

References

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