My research examines how visual information is represented and dynamically transformed in the complex neural network. A central goal is to understand how neural systems convert highly variable visual inputs into stable yet flexible representations that support perception and behavior, and how these principles relate to general intelligence. We also study how neural activity can be read out and controlled through brain–machine interfaces, linking theory to real-world applications.
Current work is organized around several themes:
Neural coding
Dynamic network
Learning rule
Brain-reader
Neural control
