Ken-ichi Morishige

Feedforward impedance control efficiently reduce motor variability.

Despite the existence of neural noise, which leads variability in motor commands, the central nervous system can effectively reduce movement variance at the end effector to meet task requirements. Although online correction based on feedback information is essential for reducing error, feedforward impedance control is another way to regulate motor variability. This Update Article reviews key studies examining the relation between task constraints and impedance control for human arm movement. When a smaller reaching target is given as a task constraint, flexor and extensor muscles are co-activated, and positional variance is decreased around the task constraint. Trial-by-trial muscle activations revealed no on-line feedback correction, indicating that humans are able to regulate their impedance in advance. These results demonstrate that not only on-line feedback correction, but also feedforward impedance control, helps reduce the motor variability caused by internal noise to realize dexterous movements of human arms. A computational model of movement planning considering the presence of signal-dependent noise provides a unifying framework that potentially accounts for optimizing impedance to maximize accuracy. A recently proposed learning algorism formulated as a V-shaped learning function explains how the central nervous system acquires impedance to optimize accuracy as well as stability and efficiency.

Practice reduces task relevant variance modulation and forms nominal trajectory.

Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points, and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.

Gaussian mixture prior distribution on artifactual current for MEG inverse problem

we observed individual neurons of the mouse brain using hard x-ray Talbottype phase-contrast micro-tomography with 1 m resolution at SPring-8. Furthermore, a nano-resolution hard x-ray Zernike-type phase-contrast microscope revealed nerve fibers and organelles including mitochondria and synapses in the neural tissue. In the near future, we will utilize that information to begin deciphering the wiring diagram of the brain by using the nano-resolution x-ray tomography.

Prediction of head-rotation movements using neck EMG signals for auditory tele-existence robot “TeleHead”

A number of works have been reported on robot control using EMG signals. Control of robots, wheelchairs, and rehabilitation aids using the arms, hands or legs by EMG signals has been quite popular and effective. However, few works have dealt with head-movement control using neck EMG signals. We have built a model that estimates continuous human head movement from neck EMG signals. Our proposed model, which considered not only static but also dynamic effects, effectively suppressed the over/undershoot, and predicted head-rotation movements well. This result indicates that the proposed model has the potential to reconstruct the observed data from neck EMG signals properly.

An analysis of MEG artifacts caused by heartbeat

Magnetoencephalography (MEG) directly measures the magnetic field caused by neural current activity, with a high temporal resolution. However, its amplitude is very weak and contaminated by various artifacts. One of the such an artifact is caused by heartbeat. In this study, we measured MEG and electrocardiogram (ECG) simultaneously. MEG was averaged with respect to an onset of ECG, that is, the peak of R-wave. Then, we applied equivalent current dipole (ECD) method to estimate current sources of artifacts caused by heartbeat. In addition, we propose a probabilistic model to remove such artifacts and applied to artificial data in order to confirm the efficiency of the method.