Ryohei Kanzaki

Ryohei Kanzaki
Research Center for Advanced Science, University of Tokyo
Tokyo, Japan

Speaker of Workshop 2

Will talk about: Brain mechanisms for the generation of adaptive behavior

Bio sketch:

Ryohei Kanzaki received his B.S., M.S. and D.Sc. degree in Neurobiology from the Institute of Biological Sciences, University of Tsukuba in 1980, 1983 and 1986, respectively. From 1987 to 1990 he was a postdoctoral research fellow at the Arizona Research Laboratories, Division of Neurobiology, University of Arizona. From 1991 to 2003 he was successively an assistant professor, associate professor, and full professor at the Institute of Biological Sciences, University of Tsukuba. From 2004 to 2006 he was a full professor at Department of Mechano-Informatics, Graduate School of Information Science and Technology, University of Tokyo. Since 2006 he is a full professor at the Research Center for Advanced Science and Technology (RCAST), University of Tokyo. He is an adjunct professor at Department of Neuroscience, University of Arizona. He is a vice president of the Japanese Society for Comparative Physiology and Biochemistry.

Talk abstract:

For many decades, neuroethology has provided insights into how nervous systems organize and generate behavior. Important contributions from work in invertebrate preparations, particularly insects, have been made to brain research in the past, expanding our general understanding of sensory and motor systems.

Insects are valuable model systems in neuroscience due to the balance between the moderate complexity of their nervous systems, a rich behavioral repertoire, and the cost of maintenance as experimental animals. Insect brains contain on the order of 105 to 106 neurons. The concept of individually identifiable neurons and small networks composing functional units have been vital for understanding insect brains. Moreover, insects are uniquely suited for multidisciplinary studies in brain research involving a combined approach at various levels, from molecules over single neurons to neural networks, behavior, modeling, and robotics, owing to their seamless accessibility to a wide variety of methodological approaches, in particular genetic engineering, neuroanatomy, electrophysiology, and functional imaging.

Adaptability, the capability to behave properly in accordance with ceaselessly changing environments, is an excellent feature of animals. Insects will become an excellent model for understanding adaptive control in biological systems and in turn, inspire control and communication in engineered systems. In this lecture, focusing on the adaptive behavior in insects, brain mechanisms of the behavior revealed by using multidisciplinary approaches will be shown. Adaptive behavior appears in the interaction between a body, brain and the environment. Therefore, an experimental system for evaluating and understanding adaptive behavior is required to be a closed-loop system, in which environmental information is fed back to an animal. This system must be capable of optionally manipulating the external environment or the properties of the animal, allowing the adaptive behavior to be systematically investigated. We have developed an insect-machine hybrid system, which moves depending on the behavioral or the neural output of an insect, as a novel experimental system. The robot is controlled by the behavior of an insect tethered on the robot or by the neural activity of the insect brain. Therefore, by arbitrarily manipulating the motion system of the robot, changes similar to those done by manipulating the sensory-motor system of the insect are possible.

At first in this lecture, as an example of adaptive behavior of an insect, odor-source orientation behavior of a male silkmoth and its neural basis will be shown. Second, the extent of adaptation in the behavioral strategy, as governed by the neural system and investigated via a robotic implementation, will be shown. Finally, I will demonstrate an insect-machine hybrid system that will lead to great insight for evaluating and understanding adaptive behaviors.

Kanzaki work image


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