AI 2 Brain : Reverse engineering the brain to understand how it learns
Our research aims to understand how cognitive control is implemented in the human brain ("AI2Brain"), thereby designing brain-inspired artificial intelligent systems that show a high level of ability to perform a wide range of tasks ("Brain2AI").
In particular, we study neural computations underlying the process of a human prefrontal cortex which allocates control over behavior to multiple types of learning and inference systems. This is achieved through a combination of computational learning theory, control theory, and experimental techniques including model-based functional magnetic resonance imaging (fMRI), electroencephalography (EEG), Transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS). Topics of interest include, but are not limited to, the following:
Theory-driven task design
Neuroscience of model-based and model-free reinforcement learning
Prefrontal meta control for learning and inference
Role of task complexity, intrinsic motivation, metacognition in prefrontal meta control