We study the biological basis of reward-based learning and decision making by monitoring, manipulating, and modeling neural activity
How do we recognize the world in a structured way (e.g., map)?
How do we remember something good and pursue it? How do we remember something bad and avoid it?
How do we make decisions when things are uncertain?
What features of the brain make one species smarter than another species?
What can we learn about how the brain works from artificial intelligence?
We address these questions by monitoring and manipulating neuronal activities using state-of-the-art techniques. We use artificial neural network modeling to compare real and artificial brains, and compare neural activities across species to understand how intelligence emerges (more info). Below is a selected summary of our scientific approaches.
If we figure out the essence of the brain, we may understand ourselves better - the origin of intelligence and consciousness. The fundamental understanding of reward-based learning and motivation will contribute to developing treatments of mental illness such as addiction and obsessive-compulsive disorder. Furthermore, we may apply the neural mechanisms of intelligent behaviors to improve current artificial intelligence technologies.