In recent years, artificial intelligence has begun to exhibit near-human performance. But are the internal computational mechanisms of these AI systems truly similar to those of the human brain? While recent studies have shown some partial correspondences, the brain possesses various characteristics that differ fundamentally from modern AI. These include rich dynamics involving oscillations and transient responses, learning driven by local neural plasticity, and the execution of information processing with remarkably low energy consumption.

By combining neuroscientific knowledge and machine learning theory, my research focuses on constructing and validating theoretical models of the dynamics and learning mechanisms within the brain’s neural circuits. To date, my work has focused heavily on the hippocampus, elucidating how its neural activity contributes to functions such as spatial navigation, memory formation, and concept acquisition. By uncovering the brain’s information processing and learning mechanisms—and examining their similarities and differences with machine learning—I ultimately aim to contribute to the future development of novel intelligent systems that leverage the unique characteristics of the brain.

If you are a student or researcher interested in brain theory or brain-inspired AI and would like to collaborate on research, please feel free to contact me.