Toward a Brain-Neuromorphics Interface.

Area of Science:

• Neuroscience
• Computer Engineering
• Materials Science

Background:

• Traditional brain-computer interfaces (BCIs) use complex, energy-inefficient CMOS technology.
• Limitations include bulky circuits and low biocompatibility, hindering human capability restoration and augmentation.

Purpose of the Study:

• To explore brain-neuromorphic interfaces (BNIs) as an advancement over traditional BCIs.
• To review recent progress in neuromorphic devices for enhanced human-machine interaction and neurorobotics.

Main Methods:

• Review of neuromorphic computing principles, including in-materia computing (e.g., VMM, reservoir computing).
• Analysis of integrated neuromorphic components for afferent nerves, efferent nerves, and sensorimotor loops.
• Discussion of compact artificial spiking neurons and bioelectronic interfaces.

Main Results:

• Neuromorphic systems offer high computational power with exceptional energy efficiency.
• Integration of neuromorphic components enables sophisticated sensorimotor capabilities for neurorobotics.
• Development of compact artificial neurons and bioelectronic interfaces facilitates seamless bioentity communication.

Conclusions:

• Brain-neuromorphic interfaces (BNIs) represent a promising future for advanced human-machine interaction.
• Continued development in neuromorphic hardware and bioelectronic interfaces is crucial for realizing sophisticated BNI applications.