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Revealing the type of information encoded by neurons activity in the motor cortex is essential not only for understanding the mechanism of motion control but also for developing a brain-machine interface. Thus far, the concept of preferred direction (PD) vector has dominated the discussion regarding how neural activity encodes information; however, a unified view of exactly what information is encoded has not yet been established. In this study, a model was constructed to describe temporal neuron activity by a dot product of the PD and the movement variables vector consisting of joint torque and angular velocity. The plausibility of this model was tested by comparing estimated neural activity with that recorded from the monkey motor cortex, and it was found that this model was able to explain the temporal pattern of neuron activity irrespective of its passive responsiveness. The mean determination coefficients of neurons that responded to proprioceptive stimuli and that responded to visual stimuli were relatively high values of 0.57 and 0.58, respectively. These results suggest that neurons in the monkey motor cortex encode state variables of the arm in a framework of modern control theory and that this information could be decoded for controlling a brain-machine interface.

State Variables of the Arm May Be Encoded by Single Neuron Activity in the Monkey Motor Cortex