横山 寛

My primary research interest is to deepen understanding of the functional role of neural dynamics related to cognition, emotion, and behavioral performance in humans by using a data-driven modeling method. With the recent development of data science technology, although computational and applied neuroscience studies on humans have attracted growing attention with various approaches, there is a huge gap between theoretical evidence and practice in social implementation. The main reason for the gap is that efficient and robust analytical methodology in a data-driven manner to interpret brain mechanisms behind measurement data has not been established. To quantitatively understand the functional mechanism in the human brain from measured neurological data, we should develop a robust analytical method to interpret the functional role in the time-evolving dynamics of a functional network of the human brain in a data-driven manner. To tackle the issue and make a breakthrough for the social implementation of applied neuroscience technology, I am engaged in the neural engineering study to develop new technology to quantitatively interpret the neural mechanism of humans by using data-driven modeling and a data-assimilation approach as my life work.