Abstract
RatCar system, a vehicle-formed brain-machine interface for a rat, has been applied to analyze bidirectional adaptation in brain and machine under direct neural connections. A rat with neural electrodes implanted in its motor cortex and basal ganglia regions was mounted on the vehicle which were designed to move around by estimating intention of the rat. Recorded neural activities, however, had been restricted to those generated nearby our electrodes, which had resulted in low accuracy of the estimation and instable control of the vehicle. In this paper, another control strategy were introduced to the system; a predefined model determined a basic operation of the vehicle (e.g., circular locomotion) while neural activities modified its global behavior. A state space representation composed the model solved by Kalman filter algorithm. This framework enabled adaptation of vehicle control mathematically dissected from adaptation in the brain. As a result, more stable and practical control of the vehicle besides observing time-varying parameters during the adaptation.
