Integrated control system using bidirectional learning between neural system and machine

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The goal of this project is to develop an integrated control system using bidirectional learning between neural system and an artificial machine, in order to attain a brain-machine interface (BMI) system which has higher control performance.Recently many research groups around the world have been focusing on BMI systems in which for examples artificial limb system or a wheelchair can be controlled with motor information derived from patients in the same way as patients control their own limbs. Neural systems have such an flexible plasticity capability that once they are connected to a BMI system they easily change their input-output characteristics. So we have focused on this brain flexibility to achieve high performance BMI systems.The research tasks we have conducted were1. Development of a recording system capable of stable measurement of neural signals chronically.2. Design and fabrication of a neural control system in which a vehicle system was controlled by neural information recorded from the motor cortex of rats3. Measurement and analysis of the plasticity of rat's brain during the chronic experiments in which variable control conditions including the feedback method and the control algorithm were applied.