Creating new functional circuits for action via brain-machine interfaces.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Cognitive Science

Background:

• Brain-machine interfaces (BMIs) are emerging technologies with potential for restorative therapies.
• BMIs create distinct functional circuits for action, separate from natural sensorimotor systems.
• Closed-loop BMI control actively engages learning and adaptation.

Purpose of the Study:

• To interpret BMIs as novel closed-loop systems for studying learning.
• To review existing BMI learning studies and their connection to motor control.
• To propose future research directions in the nascent field of BMI learning.

Main Methods:

• Review of recent motor BMI studies on skill formation and motor adaptation.
• Analysis of emerging work in sensory BMIs and other novel interface systems.
• Conceptual framework of BMIs as closed-loop learning systems.

Main Results:

• BMIs are uniquely suited for studying the learning of motor and abstract skills.
• Recent studies have shed light on neural representations of skill formation and adaptation using motor BMIs.
• Emerging research indicates BMIs can address fundamental questions in learning and sensorimotor control.

Conclusions:

• Understanding learning within BMIs can elucidate mechanisms of natural motor and abstract skill learning.
• BMI research can aid in the development of advanced neuroprosthetics.
• BMIs provide a powerful platform for investigating fundamental principles of learning and adaptation.