Maximilian Riesenhuber, Ph.D.

Associate Professor, Neuroscience at Georgetown University Medical Center

Dr. Riesenhuber received his PhD degree in computational neuroscience from Massachusetts Institute of Technology in 2000. He is currently an Associate Professor of Neuroscience at Georgetown University Medical Center in Washington, D.C.

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.

Book Chapters:

1. Riesenhuber, M, & Poggio, T. (2003). How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa & J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653.
2. Riesenhuber, M. (2005). Object recognition in cortex: Neural mechanisms, and possible roles for attention. In: Neurobiology of Attention, (Eds. L. Itti, G. Rees, & J. Tsotsos), Elsevier, 279-287.
3. Riesenhuber, M. (2009). Object categorization in man, monkey, and machine: some answers and some open questions. In:Object Categorization: Computer and Human Vision Perspectives, (Eds. S.J. Dickinson, A. Leonardis, B. Schiele, & M.J. Tarr), Cambridge University Press.

Publications:

1. Riesenhuber, M., T. Poggio (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience: 2, 1019-1025.
2. Riesenhuber, M., and T. Poggio (1999). Are cortical models really bound by the "Binding Problem"? Neuron: 24, 87-93.
3. Riesenhuber, M., and T. Poggio (2000). Models of object recognition. Nature Neuroscience: 3 supp., 1199-1205.
4. Freedman, D.J., M. Riesenhuber, T. Poggio, and E.K. Miller (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science: 291, 312-316.
5. Riesenhuber, M., and T. Poggio (2002). Neural mechanisms of object recognition. Current Opinion in Neurobiology: 12, 162-168.
6. Freedman, D.J., M. Riesenhuber, T. Poggio, and E.K. Miller (2003). A comparison of primate prefrontal and inferior temporal cortices during visual categorization. Journal of Neuroscience: 23, 5235-5246.
7. Lampl, I.., D. Ferster, T. Poggio, and M. Riesenhuber (2004). Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. Journal of Neurophysiology: 92, 2704-2713.
8. Riesenhuber, M., I. Jarudi, S. Gilad, and P. Sinha (2004). Face processing in humans is compatible with a simple shape-based model of vision. Proceedings of the Royal Society London B (Suppl.): 271, S448-450.
9. Freedman, D.J., M. Riesenhuber, T. Poggio, and E.K. Miller (2006). Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex. Cerebral Cortex: 16, 1631-1644.
10. Jiang, X., E. Rosen, T. Zeffiro, J. Van Meter, V. Blanz, and M. Riesenhuber (2006). Evaluation of a shape-based model of human face processing using fMRI and behavioral techniques. Neuron: 50, 159-172. PMID: 16600863.
11. Serre, T., L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio (2007). Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence: 29, 411-426.
12. Jiang, X., E. Bradley, R.A. Rini, T. Zeffiro, J. VanMeter, and M. Riesenhuber (2007). Categorization training results in shape- and category-selective human neural plasticity. Neuron: 53, 891-903. PMID: 17359923.
13. Cadieu, C., M. Kouh, A. Pasupathy, C. Connor, M. Riesenhuber, and T. Poggio (2007). A model of V4 shape selectivity and invariance. Journal of Neurophysiology: 98, 1733-1750. PMID: 17596412.
14. Glezer, L., X. Jiang, and M. Riesenhuber (2009). Evidence for highly selective neuronal tuning to whole words in the "Visual Word Form Area." Neuron: 62, 199-204 (2009). PMID: 19409265.
15. Riesenhuber, M., and B. Wolff (2009). Task effects, performance levels, features, configurations, and holistic face processing: A reply to Rossion. Acta Psychologica: 132, 286-292. PMID: 19665104
16. Chevillet M., Riesenhuber M. & Rauschecker J.P. (2011). Functional correlates of the anterolateral processing hierarchy in human auditory cortex. Journal of Neuroscience. 31(25): 9345-52.
17. Roy J.E., Riesenhuber, M., Poggio T., & Miller E.K. (2010). Prefrontal cortex activity during flexible categorization. Journal of Neuroscience. 30(25): 8519-28.