.png){kind=icon}
.png){kind=icon}
Return
Neural interface systems with on-device computing: machine learning and neuromorphic architectures
.png){kind=icon}
OA
AI
DOI：10.1016/j.copbio.2021.10.012.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
DEEP BRAIN-STIMULATION
NETWORK
CLASSIFICATION
WIRELESS
MEMORY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
4.9K
Citations:
1.9W