横山 寛

My primary research interest is to deepen the understanding of how neural dynamics relate to cognition, emotion, and behavioral performance in humans using data-driven modeling. With recent advances in data science technology, computational and applied neuroscience studies on humans have attracted growing attention. However, a significant gap remains between theoretical evidence and practical implementation in social contexts. This gap exists because efficient, robust analytical methodologies to interpret brain mechanisms from measurement data in a data-driven manner have yet to be established. To quantitatively understand the human brain's functional mechanisms from measured neurological data, we must develop robust analytical methods that interpret the time-evolving dynamics of the functional network. To address this and advance the social implementation of applied neuroscience technology, I am engaged in neural engineering studies. My work focuses on developing new technology that quantitatively interprets human neural mechanisms using data-driven modeling and data-assimilation approaches. This has become my life's work.