Perceiving Neural Networks

Perception builds upon complex pattern recognition abilities that are necessary to convert high-dimensional sensory signals to meaning. Natural tasks like object recognition or visual search are good examples of this process revealing the computational challenges underlying perception. To tackle these challenges complex neural networks have developed in the brain that perform surprisingly well. At the interface between artificial intelligence and neuroscience we focus on uncovering the algorithms and neuro-computational design principles of perceiving neural networks. That is, we want to explain how characteristic properties of neural systems originate from the computational requirements of specific perceptual skills:

More specifically, 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