Perceiving Neural Networks

Our research agenda lies at the interface between artificial intelligence and neuroscience as we seek to understand the neural basis of perception. Perception builds upon complex computations that are necessary to extract behaviorally relevant variables from very high-dimensional sensory input signals. For solving computationally demanding tasks such as object recognition complex neural networks have developed in the brain that perform surprisingly well. Our goal is to understand the distributed computations and neuro-computational design principles underlying these abilities. That is, we want to explain how characteristic properties of neural systems originate from the computational requirements of specific perceptual skills:

To this end, we use Machine Learning and Computational Neuroscience methods to study the problem of perceptual inference and its neural basis at different levels:

University of Tuebingen BCCN CIN MPI