Research Directions

Our goal is to understand the fundamental computational principles of the brain. We combine systems and computational neuroscience with machine learning techniques to decipher the neural codes underlying perception and cognition. In particular, we are interested in understanding how visual sensory information is represented and propagated in the mammalian brain.

Neural coding

Neural coding

Investigating information representation, transformation and transmission along visual hierarchy
Dynamic network

Dynamic network

Investigating the dynamic nature of functional networks for different brain states
Learning rule

Learning rule

Search for simple rules that guide the self-organization of the complex networks
Brain-reader

Brain-reader

Generate images directly from brain activity
Brain-machine interface

Brain-machine interface

Build close-loop BMI to test the theories of neural encoding and decoding
Deep learning models

Deep learning models

Use the state-of-art DNN models to predict brain activity and vice versa