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.
Our approach combines large-scale electrophysiological recording of neural activity, closed-loop BMI, sophisticated data analysis and state-of-the-art modeling with quantifiable behavioral tasks.
Neural coding
Dynamic network
Brain-machine interface
Deep learning models
Learning rule
