Ryoma Hattori, Ph.D.

Our laboratory focuses on understanding the neural mechanisms for decision making, reinforcement learning, and number sense in healthy and disease states. We investigate these mechanisms through large-scale recordings of neural activity and synaptic plasticity dynamics from mice engaged in sophisticated head-fixed behavior tasks. We employ data science approaches, including the use of AI, to analyze and simulate animal behaviors and neural network dynamics.

Reinforcement learning (RL) is a trial-and-error learning that involves interactions with an environment. Through the interactions, animals learn optimal behaviors by reinforcing actions that lead to rewarding events and avoiding actions that lead to undesired outcomes. RL has been also very powerful in training artificial intelligence (AI), enabling AI to outperform humans in complex tasks. Understanding of the neural mechanisms of RL facilitates not only the development of treatment for learning and cognitive impairments but also development of novel RL approach in the field of AI. However, the underlying neural mechanisms are complex because the processes involve many brain areas. We investigate how different brain areas interact to process the computations that are necessary for the RL and its associated value-based decision making, and how these processes may be impaired in neurological disorders.

Another research project in our lab focuses on the neural mechanisms for number sense. Number is an important concept in our daily lives as humans. However, the neural mechanisms for number sense have been poorly understood. We will investigate how number information is processed in the brain and how such system could be impaired in neurological disorders.