We want understand how neural systems perform visual perception. At the interface of computer vision and neuroscience, we try to understand both, how the human visual system works and how to teach computers to make sense of images. We use an interdisciplinary approach that combines methods from machine learning and computer vision with behavioral studies and neuronal population recordings in the brain. Our work is driven by the idea that we can advance artificial intelligence by understanding how biological systems implement intelligent behavior.


Neural system identification

We build upon recent advances in Deep Learning to build predictive models and understand the nonlinear computations of the early visual system.

Neural style transfer

We developed an algorithm based on deep learning that allows us to redraw a photograph in the style of an arbitrary painting with remarkable quality.

Neural mechanisms of perceptual inference

We investigate the role of top-down signals in perceptual inference tasks. In particular, we are interested in how sensory signals (bottom-up) and prior beliefs (top-down) interact in the primary visual cortex of behaving monkeys.