Evaluation on induction of driver fatigue in driving different traffic condition: An EEG study.

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.

Capacity of Autonomous Sensory Meridian Response on the Reduction of Mental Stress.

In a social environment, various types of stress can be overwhelming. Humans frequently encounter these stressful situations in social life. Stress is divided into physical stress and mental stress; the latter is induced by heavy mental workloads and has become a huge social problem, leading to mental disorders and possibly suicide in the worst scenario. Investigations into monitoring mental stress and reducing stressful conditions are, therefore, important for its prevention. In the present study, we focused on autonomous sensory meridian response (ASMR) sound, which is known to improve the human mental condition through its comforting and relaxing effects. We investigated the effect of ASMR on the mental workload induced by mental tasks by the evaluation of EEG activation patterns in normal subjects. Our results showed a significant decrease in alpha-band activity and a significant increase in gamma (high beta)-band activity under the induction of mental workload by mental tasks compared to the resting condition. When applying ASMR sound, alpha- and gamma-band activity under the induction of mental workload by mental tasks was restored to the level of the resting condition. In conclusion, these results indicate that ASMR sound reduces the mental stress induced by mental workload.

The Effects of Driving Experience on the P300 Event-Related Potential during the Perception of Traffic Scenes.

The incidence of human-error-related traffic collisions is markedly reduced among drivers who have few years of driving experience compared with those with little driving experience or fewer driving opportunities, even if they have a driver's license. This study analyzes the effect of driving experience on the perception of the traffic scenes through electroencephalograms (EEGs). Primarily, we focused on visual attention during driving, the essential visual function in the visual search and human gaze, and evaluated the P300, which is involved in attention, to explore the effect of driving experience on the visual attention of traffic scenes, not for improving visual ability. In the results, the P300 response was observed in both experienced and beginner drivers when they paid visual attention to the visual target. Furthermore, the latency for the peak amplitude of the P300 response among experienced drivers was markedly faster than that in beginner drivers, suggesting that the P300 latency is a piece of crucial information for driving experience on visual attention.

Realistic Mathematical Model of Retinal Outer Plexiform Layer for Edge Detection.

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.

Roles of the Cerebellar Interneurons on Vestibulo-Ocular Reflex Motor Learning: A Computational Study.

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.

Realization of Direction Selective Motor Learning in the Artificial Cerebellum: Simulation on the Vestibuloocular Reflex Adaptation.

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.

Computational Theory Underlying Acute Vestibulo-ocular Reflex Motor Learning with Cerebellar Long-Term Depression and Long-Term Potentiation.

The vestibulo-ocular reflex (VOR) can be viewed as an adaptive control system that maintains compensatory eye movements during head motion. As the cerebellar flocculus is intimately involved in this adaptive motor control of the VOR, the VOR has been a popular model system for investigating cerebellar motor learning. Long-term depression (LTD) and long-term potentiation (LTP) at the parallel fiber-Purkinje cell synapses are considered to play major roles in cerebellar motor learning. A recent study using mutant mice demonstrated cerebellar motor learning with hampered LTD; the study concluded that the parallel fiber-Purkinje cell LTD is not essential. More recently, multiple forms of plasticity have been found in the cerebellum, and they are believed to contribute to cerebellar motor learning. However, it is still unclear how synaptic plasticity modifies the signal processing that underlies motor learning in the flocculus. A computational simulation suggested that the plasticity present in mossy fiber-granule cell synapses improves VOR-related sensory-motor information transferred into granule cells, whereas the plasticity in the molecular layer stores this information as a memory under guidance from climbing fiber teaching signals. Thus, motor learning and memory are thought to be induced mainly by LTD and LTP at parallel fiber-Purkinje cell synapses and by rebound potentiation at molecular interneuron-Purkinje cell synapses among the multiple forms of plasticity in the cerebellum. In this study, we focused on the LTD and LTP at parallel fiber-Purkinje cell synapses. Based on our simulation, we propose that acute VOR motor learning accomplishes by simultaneous enhancement of eye movement signals via LTP and suppression of vestibular signals via LTD to increase VOR gain (gain-up learning). To decrease VOR gain (gain-down learning), these two signals are modified in the opposite directions; namely, LTD suppresses eye movement signals, whereas LTP enhances vestibular signals.

Reprint of: Simulation Platform: a cloud-based online simulation environment.

For multi-scale and multi-modal neural modeling, it is needed to handle multiple neural models described at different levels seamlessly. Database technology will become more important for these studies, specifically for downloading and handling the neural models seamlessly and effortlessly. To date, conventional neuroinformatics databases have solely been designed to archive model files, but the databases should provide a chance for users to validate the models before downloading them. In this paper, we report our on-going project to develop a cloud-based web service for online simulation called "Simulation Platform". Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators such as GENESIS, NEURON and NEST, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user remotely accesses to the machine through a web browser and carries out the simulation, without the need to install any software but a web browser on the user's own computer. Therefore, Simulation Platform is expected to eliminate impediments to handle multiple neural models that require multiple software.

Simulation Platform: a cloud-based online simulation environment.

For multi-scale and multi-modal neural modeling, it is needed to handle multiple neural models described at different levels seamlessly. Database technology will become more important for these studies, specifically for downloading and handling the neural models seamlessly and effortlessly. To date, conventional neuroinformatics databases have solely been designed to archive model files, but the databases should provide a chance for users to validate the models before downloading them. In this paper, we report our on-going project to develop a cloud-based web service for online simulation called "Simulation Platform". Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators such as GENESIS, NEURON and NEST, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user remotely accesses to the machine through a web browser and carries out the simulation, without the need to install any software but a web browser on the user's own computer. Therefore, Simulation Platform is expected to eliminate impediments to handle multiple neural models that require multiple software.

A model-based theory on the signal transformation for microsaccade generation.

The eyes are continuously fluctuating even during fixation. The fluctuations are called miniature eye movements and consist of microsaccades, drifts, and tremors. It has been revealed that these miniature eye movements aid our vision; they improve the visibility of high spatial frequency components, and prevent retinal adaptation during fixation. Although the functional roles of the miniature eye movements have gradually been uncovered, their generation mechanism remains a mystery. Here, we focused on microsaccades, and constructed a neuronal network model to explore their generation mechanism. Several lines of evidence ensure that microsaccades and saccades share the same neuronal circuitry because they fall on the same main sequence, a relationship between their amplitudes and peak velocities. In the saccade pathway, saccade commands generated in the superior colliculus are relayed to motoneurons via burst neurons (BNs) and the integrator network. The BNs are inhibited by omnipause neurons (OPNs) except when saccades are generated. We configured a model for microsaccades based on the well-defined saccade neuronal pathway including tonic neurons, BNs, OPNs, the integrator network, and the eye plant. The model successfully reproduced various characteristics of microsaccade: square-wave jerk, single-sided microsaccades, and the main sequence. Moreover, during microsaccades, BNs showed low-rate spikes due to a partial release from the OPN inhibition. These results suggest that microsaccades are generated when BNs are partially, but not completely, released from tonic inhibition by OPNs during fixation, in contrast to the generation of ordinary saccades in which OPNs pause firing and release BNs from their strong inhibition.

PLATO: data-oriented approach to collaborative large-scale brain system modeling.

The brain is a complex information processing system, which can be divided into sub-systems, such as the sensory organs, functional areas in the cortex, and motor control systems. In this sense, most of the mathematical models developed in the field of neuroscience have mainly targeted a specific sub-system. In order to understand the details of the brain as a whole, such sub-system models need to be integrated toward the development of a neurophysiologically plausible large-scale system model. In the present work, we propose a model integration library where models can be connected by means of a common data format. Here, the common data format should be portable so that models written in any programming language, computer architecture, and operating system can be connected. Moreover, the library should be simple so that models can be adapted to use the common data format without requiring any detailed knowledge on its use. Using this library, we have successfully connected existing models reproducing certain features of the visual system, toward the development of a large-scale visual system model. This library will enable users to reuse and integrate existing and newly developed models toward the development and simulation of a large-scale brain system model. The resulting model can also be executed on high performance computers using Message Passing Interface (MPI).

Method for the construction and use of carbon fiber multibarrel electrodes for deep brain recordings in the alert animal.

Microiontophoresis of neuroactive substances during single unit recording in awake behaving animals can significantly advance our understanding of neural circuit function. Here, we present a detailed description of a method for constructing carbon fiber multibarrel electrodes suitable for delivering drugs while simultaneously recording single unit activity from deep structures, including brainstem nuclei and the cerebellum, in the awake behaving primate. We provide data that should aid in minimizing barrel resistance and the time required to fill long, thin multibarrel electrodes with solutions. We also show successful single unit recording from a variety of areas in the awake squirrel monkey central nervous system, including the vestibular nuclei, Interstitial Nucleus of Cajal, and the cerebellum. Our descriptions and data should be useful for investigators wishing to perform single unit recordings during microiontophoresis of neuroactive substances, particularly in deep structures of animals with chronically implanted recording chambers.