Brain 2 AI
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:
- Meta-control of reinforcement learning
- Meta-cognitive learning
- Introspective learning*
- AI experimenter*
- AI-human co-evolution engine* : AI to boost human intelligence
- Meta BCI* : meta-control theory-based brain-computer interface
*We intentionally used the above keywords to confuse readers.