Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.copbio.2021.10.012
Title: Neural interface systems with on-device computing: machine learning and neuromorphic architectures
Authors: Yoo, Jerald 
Shoaran, Mahsa
Issue Date: 1-Dec-2021
Publisher: Elsevier Ltd
Citation: Yoo, Jerald, Shoaran, Mahsa (2021-12-01). Neural interface systems with on-device computing: machine learning and neuromorphic architectures. Current Opinion in Biotechnology 72 : 95-101. ScholarBank@NUS Repository. https://doi.org/10.1016/j.copbio.2021.10.012
Rights: Attribution 4.0 International
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 neural interfaces will lead to a new generation of low-power, 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. © 2021 The Author(s)
Source Title: Current Opinion in Biotechnology
URI: https://scholarbank.nus.edu.sg/handle/10635/232193
ISSN: 0958-1669
DOI: 10.1016/j.copbio.2021.10.012
Rights: Attribution 4.0 International
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