Maximilian Riesenhuber, Ph.D.

In his research, Dr. Riesenhuber investigates the neural mechanisms underlying object recognition and learning in cortex, combining computational modeling with human behavioral, EEG and fMRI experiments. This comprehensive approach addresses one of the major challenges in neuroscience today, that is, the necessity to combine experimental data from a range of approaches in order to develop a rigorous and predictive model of human brain function that quantitatively and mechanistically links neurons to behavior. This is of interest not only for basic research, but also for the investigation of the neural bases of behavioral deficits in mental disorders. Understanding the neural mechanisms underlying object recognition in the brain is also of significant relevance for Artificial Intelligence, as the capabilities of pattern recognition systems in engineering (e.g., in machine vision or speech recognition) still lag far behind that of their human counterparts in terms of robustness, flexibility, and the ability to learn from few exemplars. Finally, a mechanistic understanding of the neural processes endowing the brain with its superior object recognition abilities opens the door to supporting and extending human cognitive abilities in this area through hybrid brain-machine systems ("augmented cognition").

Dr. Riesenhuber has received several awards, including a McDonnell-Pew Award in Cognitive Neuroscience, Technology Review's TR100, and an NSF CAREER Award.