|1.||Regarding computational neurobiology, an attempt is being made to bridge the gaps between computational theories and neurobiological reality in the form of biologically plausible network models. The models can be of different physical levels, from macroscopic ones that account for the functions of different brain areas based on lesion and imaging studies, to detailed ones that take into account cellular and molecular data about the local circuitry and plasticity. These models are not only testable experimentally but provide novel experimental designs crucial for understanding the brain's computational mechanisms. The research subjects include: 1) learning and control of sequential action through reinforcement (basal ganglia, cerebellum, cortical motor areas); 2) functions of local circuit, intra cellular dynamics, and neuromodulators in motor learning (cerebellum, striatum); 3) representations of complex temporal patterns in the brain for perception and motion generation (in songbirds and humans); 4) models of emotion and consciousness as the global regulatory system of the brain.|
|2.||Regarding computational psychology, sensory-motor control research has recently come to provide a comprehensive account of how the central nervous system (CNS) may plan purposeful acts starting from sensory input to motor output. Thus, computational theories and models concerning human motor control and learning are being examined using behavioral (psychological) experiments and non-invasive methods of investigating human brain activities (e.g. fMRI, PET and MEG). Hopefully, it will become possible: 1) to identify how, where, and when computational problems of motor control and learning (e.g., trajectory planning, coordinate transformation, generation of motor commands, and acquisition of internal models) are solved in the CNS, and 2) to investigate whether human subjects can learn optimization principles in trajectory planning and whether the principles contribute to the perception of the human body motion. Also, the biological plausibility of computational models and robotic simulations proposed by the project are being examined using these methods.|
|3.||The third research area, computational
learning, is seeking to obtain an understanding of sensory-motor
coordination on a system's level, particularly using statistical,
physical, and mathematical modeling. The general research goals are to
obtain insight into the theoretical constraints of movement
generation, to device algorithms of how particular problems of motor
control and learning can be solved, and to validate these algorithms
by synthesizing behavior with anthropomorphic robot hardware.|
Much effort is being devoted to comparisons of synthesized behavior with human behavior in collaboration with the computational psychology approach, as well as joint studies of functional models of brain data with computational neurobiology. Research projects include topics of statistical learning, reinforcement learning, and neural network learning for problems of sensory-motor control, topics of dynamical systems theory for motor pattern generation, and the study of learning from demonstration in light of movement primitives, movement segmentation, and the sequencing of complex motor acts.
It is believed that if the function of the brain can be understood, it should become possible to implement it in a robot, or any other form of artificial machine. Thus, constructing and programming humanoid robots that can incorporate the computational models and algorithms developed to explain learning in sensory-motor integration is being actively pursued.