Neural Computation Unit
Professor Kenji Doya
The Neural Computation Unit pursues the dual goals of developing robust and flexible learning algorithms and elucidating the brain’s mechanisms for robust and flexible learning. Our specific focus is on how the brain realizes reinforcement learning, in which an agent, biological or artificial, learns novel behaviors in uncertain environments by exploration and reward feedback. We combine top-down, computational approaches and bottom-up, neurobiological approaches to achieve these goals.
We work on three major subjects:
- 1) development of a novel computational framework for system identification of biological networks;
- 2) neurobiological experiments to study the dynamic functions of neuromodulators in regulating adaptive behaviors;
- 3) robotic experiments to explore adaptive mechanisms necessary for survival and reproduction in dynamic environments.
By combining theoretical, biological, and engineering approaches, the research shall produce novel software tools for dynamic modeling, highly adaptive robots with emotion-like regulatory functions, and new approaches to therapy and prevention of psychiatric disorders.