I'm a neuroscientist in Computational Neuroscience unit at Okinawa Institute of Science and Technology.
I am interested in how various building blocks of neural systems work together, broadly speaking. The main focus of my research is uncovering how biophysical mechanisms in neurons and neural networks impact their functions. A neural system typically has a vast repertoire of physiological mechanisms interacting with each other. Such complexity somehow enables the whole system to function in a specific "operating mode" that performs certain information processing. This phenomenon raises many questions, such as how we can characterize operating modes of neural systems, how we can find which cellular mechanisms are responsible, etc. To investigate these, I extensively use computational modeling. This powerful tool allows us to explore systems often far beyond the regime accessible to available experiments and efficiently generate experimentally testable hypotheses. However, building a model based on experimental data is rarely a simple task, and ideas from statistical learning theory have proven useful for overcoming difficulties in model building. I have tried to bring together the approaches to this whole range of problems and contribute to our understanding of the physical basis of neural information processing.
To find out more, please check out the full list of publications.
I have developed the following computer models and data analysis packages:
I have been teaching how to build computational models of neural systems. Here are some materials and codes for the classes