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Neural interface systems with on-device computing: machine learning and neuromorphic architectures

Cited 17 time in Web of Science Cited 22 time in Scopus
Authors

Yoo, Jerald; Shoaran, Mahsa

Issue Date
2021-12
Publisher
Elsevier BV
Citation
Current Opinion in Biotechnology, Vol.72, pp.95-101
Abstract
Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developments target pervasive implantables. Along with advances in electrodes, neural recording, and neurostimulation circuits, integration of disease biomarkers and machine learning algorithms enables real-time and on-site processing of neural activity with no need for power-demanding telemetry. This recent trend on combining artificial intelligence and machine learning with modern neuralinterfaceswill leadto a new generationof lowpower, smart, and miniaturized therapeutic devices for a wide range of neurological and psychiatric disorders. This paper reviews the recent development of the 'on-chip' machine learning and neuromorphic architectures, which is one of the key puzzles in devising next-generation clinically viable neural interface systems.
ISSN
0958-1669
URI
https://hdl.handle.net/10371/200789
DOI
https://doi.org/10.1016/j.copbio.2021.10.012
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Yoo, Jerald유담
부교수
  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

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