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Brain-Machine Interface-Based Rat-Robot Behavior Control.

Jiacheng Zhang^1,2,3, Kedi Xu^4,5,6, Shaomin Zhang^1,2,3

¹Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, People's Republic of China.

Advances in Experimental Medicine and Biology

|November 16, 2019

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This summary is machine-generated.

Brain-machine interfaces (BMIs) enable controlling animal locomotion, transforming them into bio-robots. This research explores rat-robot navigation systems, integrating biological and artificial intelligence for advanced control.

Area of Science:

Neuroscience
Robotics
Bioengineering

Background:

Brain-machine interfaces (BMIs) establish bidirectional communication between the brain and external devices.
The machine-to-brain pathway allows artificial information input, influencing neural activity and generating sensations.
BMI technology can precisely control animal locomotion, creating bio-robotic systems.

Purpose of the Study:

To review laboratory work on rat-robot navigation systems.
To describe the principles, control sites, and remote system design for rat-robot navigation.
To introduce advancements in optogenetics and "mind-controlled" rat navigation.

Main Methods:

Utilized specific brain site stimulations for locomotion control in rats.
Developed a remote control system for the rat-robot navigation.

Keywords:

Behavior control Brain stimulation Brain-machine interface Rat-robot

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Implemented optogenetic technologies for enhanced modulation control and integrated artificial intelligence with biological intelligence.

Main Results:

Successfully demonstrated precise control over rat locomotion, enabling robot-like navigation.
Developed a "mind-controlled" rat navigation system.
Integrated biological and artificial intelligence through image and voice-assisted automatic control and training systems.

Conclusions:

Rat-robot navigation systems offer a platform for precise locomotion control and bio-robot creation.
Combining biological and artificial intelligence opens avenues for advanced control and training.
Future developments include environmental information acquisition and write-in BMIs for enhanced applications.