Robots controlled by neural networks trained based on brain signals

Recent works on brain machine interface (BMI) has given promising results for developing prosthetic devices aimed at restoring motor functions in paralyzed patients. The goal of this work is to create a part mechanical, part biological robot that operates on the basis of the neural activity of rat brain cells. In our method, first the rat learns to move the robot by pressing the right and left lever in order to get food. Then, we utilize the data of multi-electrode recordings to train artificial neural controllers, which are later employed to control the robot motion based on the brain activity of rats. The results show a good performance of artificial neural network controlling the real robot.