B05 Research Group

B05-1 Elucidation of the mechanism of motor synergy emergence in deep reinforcement learning

Research Outline

It is known that motor synergies are used in human motor control, but what kind of law the central nervous system is computationally based on and what mechanism it is generated in is not reveiled. The mathematical model for synergy emergence has not been built yet. In computational neuroscience, an optimization method that minimizes some cost function is proposed. However, a mathematical model of the environment and body is priorly required for such optimization. Until now, few have dealt with the mechanism of what kind of computational principle can generate synergies under unknown environment case. We deepen our understanding of the mathematical mechanism as a nonlinear dynamics system, and verify its expandability and effectiveness when it is integrated with deep learning and adapted to multi-degree-of-freedom mechanical dynamics.

Members

Principal Investigator	Mitsuhiro HAYASHIBE	Professor, Tohoku University
Co-investigators	Kyo KUTSUZAWA	Assistant Professor, Tohoku University
	Akito FUKUNISHI	Master Student, Tohoku University
	Ahmed HANNAN	Ph.D Student, Tohoku University
	Li GUANDA	Ph.D Student, Tohoku University
	Taku SUGIYAMA	Master Student, Tohoku University

B05-2 Adaptation ability of human postural control system revealed by a closed-loop electrical muscle stimulation system

Research Outline

This study aims to clarify the mechanisms of how the brain learns to stabilize the unstable human standing posture. Firstly, we will develop a novel closed-loop electrical stimulation system to modulate the external ankle joint torque depending on the position and velocity of the center of body mass. This system enables to artificially alter the body dynamics of human standing posture without using any special perturbation device. Secondly, we investigate the human postural control system’s adaptation process to a novel dynamics that the new system imposes by examining the pattern of spontaneous body sway and the response to the sensory and physical perturbations.

Members

Principal Investigator	Daichi NOZAKI	Professor, The University of Tokyo
Co-investigators	Syota HAGIO	Lecturer, Kyoto University

B05-3 Mechanism underlying the hyper-adaptation of bipedal locomotion to the evolutionary change of the foot.

Research Outline

Foot is the most drastically altered part of the human body in the course of the human evolution. The fact that the human foot is specialized not only indicates that modification of the foot structure is essential for acquisition of stable, efficient human bipedal locomotion, but also suggests that the human nervous system possesses an ability to adaptively reorganize itself so that the change in the body morphology can be recognized and effectively utilized to facilitate the control of bipedal locomotion. In this study, we try to elucidate such hyper-adaptive mechanism of human bipedal locomotion based on a neuro-musculoskeletal forward dynamic simulation.

Members

Principal Investigator

Naomichi OGIHARA

Professor, The University of Tokyo

B05-4 Understanding neural manifold of the movements using human neuroimaging and non-invasive brain stimulation

Research Outline

The purpose of this research is to examine neural representation (manifold) related to movement variability in humans. To achieve this purpose, we are going to conduct an EEG (electroencephalogram) experiment with motor tasks and identify the low-dimensional representation of EEG activity during the movements. Furthermore, we are going to try to manipulate such the representation using a brain stimulation technique.

Members

Principal Investigator	Isao NAMBU	Associate Professor, Nagaoka University of Technology
Co-investigators	Yasuhiro WADA	Professor, Nagaoka University of Technology
	Hiroshi YOKOYAMA	Project Assistant Professor, National Institute for Physiological Sciences

B05-5 Development of motor learning model that can reuse partial dynamics based on estimation of transformation between mappings

Research Outline

Human can recover from its partial disorder of its body or brain function by reusing neural circuits that were obtained in the past. This process of partial reuse of its brain function is gathering interest for better understanding of the hyper-adaptivity. The objective of the research is to propose a motor-learning model based on an idea of dependency estimation among multimodal sensors and estimation of transformation between mappings.
Motor learning of left and right arms is considered, whose partial dynamics has a specific relationship of symmetry. The property of the estimation of transformation of mappings is expected to detect the relationship after the dependency-based motor learning scheme independently learn partial dynamics. When partial disorder of the left arm controller occurs, for example, partial dynamics previously obtained by the proposed model is expected to be used to compensate the disordered function through the obtained transformation.

Members

Principal Investigator	Yuichi KOBAYASHI	Associate Professor, Shizuoka University
Co-investigators	Taisei MATSUURA	Undergraduate Student, Faculty of Engineering
	Sota NAKAMURA	Undergraduate Student, Faculty of Engineering

B05-6 Shared-control of teleoperated robot maintaining operator’s embodiment under intervention of AI

Research Outline

In this study, we develop an operation interface which enables an operator to control a robotic arm, feeling a sense of agency and embodiment of the robot, while the robot motion is intervened and corrected by a supervisor with environment recognition function. The embodiment and feeling of the agency during robot control can be expected to maximize human adaptability to operate the robotic arm, eliciting control skills of human own body. As a result, the dexterity of the arm and success rate of task completion will be improved by aggregating human skill and AI-based recognition and control ability.

Members

Principal Investigator	Yasuhisa HASEGAWA	Professor, Nagoya University
Co-investigators	Tomoya MORITA	Nagoya University

B05-7 Systems engineering approach for understanding supraspinal mechanisms of the intermittent feedback control during human upright stance

Research Outline

In this study, we aim to understand supraspinal mechanisms of the intermittent feedback control during human upright stance by integrating postural responses, EEG/EMG responses, TMS responses for evaluating corticospinal excitability, and rTMS-based neuromodulations of those responses to mechanical perturbations.

Members

Principal Investigator	Taishin NOMURA	Professor, Osaka University
Co-investigators	Yasuyuki SUZUKI	Lecturer, Osaka University
	Akihiro NAKAMURA	PH.D Student, Osaka University
	Matija Milosevic	Assistant Professor, Osaka University
	Kimitaka NAKAZAWA	Professor, The University of Tokyo
	Saburo SAKODA	Honorary Director, National Hospital Organization Osaka Toneyama Medical Center
	Takuyuki ENDO	Doctor, National Hospital Organization Osaka Toneyama Medical Center

B05-8 A reinforcement learning model with dynamic state space that enables adaptation to indefinite environments

Research Outline

There are two types of uncertainty. There are two types of uncertainty. One has a fixed state space where which state is taken is determined stochastically like a dice. The other is the one in which the state space is not fixed. The environment with which the state space cannot be defined is called an indefinite environment. The purpose of this research is to construct and verify a reinforcement learning model with dynamic state space that enables adaptation to indefinite environments. Specifically, we will construct a model that performs a task called the two-target search task in real time, and verify it by analyses of neuronal activities, that is, clarify where in the brain the constituent elements of the model correspond. The task requires to fixate one of the four light spots, and two adjacent light spots serve as the correct target alternately in each trial (correct pair). After the subject correctly fixates the target a certain number of trials in succession based on the knowledge of the correct pair, another two light spots become a correct pair, and the subject has to find it by searching. In this sense, this task is suitable for studying the exploration-exploitation trade-off, which is an important problem in reinforcement learning. The reinforcement learning model with dynamic state space can perform the two-target search task by making the state space, which is fixed in the ordinary reinforcement learning model, dynamic.

Members

Principal Investigator	Kazuhiro SAKAMOTO	Associate Professor, Tohoku Medical and Pharmaceutical University
Co-investigators	Hajime MUSHIAKE	Professor, Tohoku University
	Satoshi ZUGUCHI	PH.D Student, Osaka University

B05-9 Attention control training based on tailor-made neurofeedback system for facilitating motor learning in elderly

Research Outline

This research project aims to establish a tailor-made neurofeedback training system for improving cognitive function in the elderly by using visualization method for the individual differences in attentional ability from behavioral data and brain activity. I expect that the proposed tailor-made training can improve adaptation and motor learning abilities in the elderly. Finally, I try to propose an efficient intervention protocol based on a computational model for predicting individual training effect.

Members

Principal Investigator

Takeshi SAKURADA

Assistant Professor, Ritsumeikan University

B05-10 Modeling of the motor recovery process and optimization of rehabilitation strategy using VR

Research Outline

Conventional rehabilitation for motor recovery tends to be designed by physiotherapists and doctors. Since the physiotherapist observes the progress status of the rehabilitation and the reaction of the patient and estimates the state of the motor recovery, it is required to deal with many subjective and empirical factors to determine the rehabilitation policy.
This study aims to model such an interaction process between physiotherapists and patients to provide the most appropriate rehabilitation policy for each patient.

Members

Principal Investigator	Tetsunari INAMURA	Associate Professor, National Institute of Informatics
Co-investigators	Fuminari KANEKO	Project Associate Professor, Keio University

B05-11 Developmentalhyper-adaptability of sensorimotor dynamics under rapid growth

Research Outline

In early developmental phase, human shows rapid physical development and neural maturation, while at the same time humans would acquire sensorimotor dynamics that form the foundation of motor controls or behaviours in the later life. In this study, we try to understand the developmental mechanisms of sensorimotor dynamics and behavioural emergence by modelling the infantile spontaneous movements, the embodiment of the musculoskeletal system, and learning rules in the nervous system.

Members

Principal Investigator	Hoshinori KANAZAWA	Research Assistant Professor, The University of Tokyo
Co-investigators	Yasuo KUNIYOSHI	Professor, The University of Tokyo
	Dongmin KIM	Graduate Student, The University of Tokyo
	Masahiko KAWAI	Project Associate Professor, Kyoto University
	Yohei NOMOTO	Graduate Student, The University of Tokyo
	Akito YOSHIDA	Graduate Student, The University of Tokyo