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

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.

Automated summary

The development of neural interface and brain-machine interface systems has opened up new possibilities for treating various neurological disorders through the integration of artificial intelligence and machine learning. This trend towards combining advanced technologies will lead to the creation of low-power, smart, and miniaturized therapeutic devices for a wide range of neurological and psychiatric disorders.