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Fatigue is the one of major causes of traffic accidents. Causes of driver fatigue can be divided into physical factors such as lack of sleep, long duration of driving, and a large amount of driving maneuvers, and mental factors such as recognition of traffic scenes to need to drive. Among those factors, a lot of studies on fatigue of drivers focused on the lack of sleep, and long duration of driving and induction of fatigue. However, little attention has been paid to the amount of driving maneuvers and recognition of traffic scenes on the induction of fatigue of drivers even both mental and physical factor related to driving. In this study, we created different traffic scenarios in terms of tuning traffic density and/or driving area to control the amount of driving maneuvers and recognition of traffic environment, and we evaluate the theta and alpha band EEG responses which are known to the one of biological index reflecting fatigue to investigate drivers' fatigue by driving in such different traffic condition.

The vestibule-ocular reflex (VOR) has been one of the most popular model systems to investigate the role of the cerebellum in adaptive motor control. The cerebellum receives sensory and motor information via mossy and climbing fibers, and solely outputs motor-related activities from Purkinje cell. The cerebellar flocculus also contains rich inhibitory interneurons known as Golgi, basket, and stellate cells. While most of the previous studies on VOR motor learning have focused on responses of Purkinje cells, little attention has been paid to the roles of cerebellar inhibitory interneurons due to difficulty in identifying and recording them in behaving animals. Thus, the contribution of those interneurons on cerebellar motor learning is still a mystery. Herein, we have constructed a computational model of the VOR that explicitly implements the anatomically realistic floccular neuronal network structure so that the activities of each inhibitory interneuron can be evaluated. From the knocked out simulation of Golgi or basket/stellate cells, we confirmed that those inhibitory interneurons play a different contribution to the acquisition of new VOR performance.

Edge information is essential for object recognition and motion detection. It is reported that photoreceptors, horizontal cells and bipolar cells in the outer retina involved for edge detection. Moreover, it is known that the center and the surround receptive field structure found in the bipolar cell layer is thought to be related to an initial process of edge detection. In the present study, we constructed retinal network models including photoreceptors, horizontal cells, and bipolar cells using single-compartment neurons to investigate those contributions for edge detection. We simulate fixation of a natural image with changing the size of the horizontal cell receptive field and confirmed that the constructed model successfully extracts edges in the image. Furthermore, most of the edge in the scene is extracted when the size of the horizontal cell receptive field matched with that reported in anatomical evidence. To evaluate the performance of edge detection, we compare the result of edge detection with the Canny algorithm. As a result, we conformed that the model well detects fine edges similar to the Canny edge detection.

The vestibule-ocular reflex (VOR) has been one of the most popular model systems to investigate the role of the cerebellum in adaptive motor control. VOR motor learning can be experimentally induced by continuous application of combination of head rotating stimulus and optokinetic stimulus. For instance, in phase application of those stimuli decreases VOR gain defined by eye velocity of VOR in the dark divided by head velocity, while out of phase of those increases VOR gain. It has been known that VOR gain is modifiable context-dependently. Namely, VOR gains for leftward and rightward head rotations can be respectively increased and decreased simultaneously. The cerebellar signal processing underlying the context dependent VOR motor learning, however, is not fully uncovered. In the present study, we simulated direction selective VOR motor learning, using the artificial cerebellar neuronal network model that we developed to understand the origin of the cerebellar motor learning.