The Laboratory for Computational
Cognitive Neuroscience

Maximilian Riesenhuber, PhD

Publications

for reprints, contact

• Journal Articles
• Book Chapters
• Technical Reports
• Conference Papers (selection, not up-to-date)

Journal Papers

• Scholl, C.A., Jiang, X., Martin, J.G., Riesenhuber, M. (2013). Time Course of Shape and Category Selectivity Revealed by EEG Rapid Adaptation. Journal of Cognitive Neuroscience. February 2014 26(2): 408-421.
• Glezer, L.S., Riesenhuber, M. (2013). Individual Variability in Location Impacts Orthographic Selectivity in the “Visual Word Form Area”. Journal of Neuroscience. 3 July 2013 33(27): 11221-11226.
• Chevillet, M.A., Jiang, X., Rauschecker, J.P., Riesenhuber, M. (2013). Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream. Journal of Neuroscience. 20 March 2013 33(12): 5208-5215.
• Jiang, X., Bollich, A., Cox, P., Hyder, E., James, J., Gowani, S.A., Hadjikhani, N., Blanz, V., Manoach, D.S., Barton J.J.S., Gaillard W.D., Riesenhuber, M. (2013). A quantitative link between face discrimination deficits and neuronal selectivity for faces in autism. NeuroImage: Clinical. 26 February 2013 2(1): 320-331.
• Chevillet M., Riesenhuber M. & Rauschecker J.P. (2011). Functional Correlates of the Anterolateral Processing Hierarchy in Human Auditory Cortex. J Neurosci. 2011 Jun 22 31(25): 9345-52.
• Roy J.E., Riesenhuber, M., Poggio T., & Miller E.K. (2010). Prefrontal Cortex Activity during Flexible Categorization. J Neurosci. 2010 Jun 23 30(25): 8519-28.
• Riesenhuber, M., & Wolff, B.S. (2009). Task effects, performance levels, features, configurations, and holistic face processing: A reply to Rossion. Acta Psychologica 132(3): 286-292.
• Glezer, L.S., Jiang, X., & Riesenhuber, M. (2009). Evidence for Highly Selective Neuronal Tuning to Whole Words in the “Visual Word Form Area”. Neuron 62: 199-204.
• Cadieu, C., Kouh, M., Pasupathy, A., Conner, C., Riesenhuber, M., & Poggio, T.A. (2007). A Model of V4 Shape Selectivity and Invariance. J Neurophysiol 98: 1733-1750.
• Riesenhuber, M. (2007). Appearance Isn't Everything: News on Object Representation in Cortex. Neuron 55: 341-344.
• Jiang, X., Bradley, E., Rini, R.A., Zeffiro, T., VanMeter, J., & Riesenhuber, M. (2007). Categorization Training Results in Shape- and Category-Selective Human Neural Plasticity. Neuron 53(6): 891-903.
• Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T. (2007). Robust Object Recognition with Cortex-like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3): 411-426.
• Freedman, D.J., Riesenhuber, M., Poggio, T., & Miller, E.K. (2006). Experience-Dependent Sharpening of Visual Shape Selectivity in Inferior Temporal Cortex. Cereb. Cortex 16: 1631-1644.
• Jiang, X., Rosen, E., Zeffiro, T., VanMeter, J., Blanz, V., & Riesenhuber, M. (2006). Evaluation of a Shape-Based Model of Human Face Discrimination Using fMRI and Behavioral Techniques. Neuron 50: 159-172.
• Riesenhuber, M., Jarudi, I., Gilad, S., & Sinha P. (2004). Face processing in humans is compatible with a simple shape-based model of vision. Proc. R. Soc. Lond. B (suppl.) 271: S448-450.
• Lampl, I., Ferster, D., Poggio, T., & Riesenhuber, M. (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.
• Riesenhuber, M. (2004). An action video game modifies visual processing. Trends in Neurosciences 27: 72-74.
• Freedman, D.J., Riesenhuber, M., Poggio, T., & Miller, E.K. (2003). A Comparison of Primate Prefrontal and Inferior Temporal Cortices during Visual Categorization. Journal of Neuroscience 23: 5235-5246.
• Riesenhuber, M., & Poggio, T. (2002). Neural Mechanisms of Object Recognition. Current Opinion in Neurobiology 12: 162-168.
• Freedman, D.J., Riesenhuber, M., Poggio, T., & Miller, E.K. (2002). Visual Categorization and the Primate Prefrontal Cortex: Neurophysiology & Behavior. Journal of Neurophysiology 88: 930-942.
• Freedman, D.J., Riesenhuber, M., Poggio, T., & Miller, E.K. (2001). Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex. Science 291: 312-316.
• Riesenhuber, M. & Poggio, T. (2000). Models of Object Recognition. Nature Neuroscience 3(supp.): 1199-1204.
• Riesenhuber, M. & Poggio, T. (1999). Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience 2: 1019-1025.
• Riesenhuber, M. & Poggio, T. (1999). Are Cortical Models Really Bound by the 'Binding Problem'? Neuron 24: 87-93.
• Riesenhuber, M., Bauer, H.-U., Brockmann, D., & Geisel, T. (1998). Breaking Rotational Symmetry in a Self-Organizing Map-Model for Orientation Map Development. Neural Computation 10: 717-730.
• Bauer, H.-U., Riesenhuber, M., & Geisel, T. (1996). Phase diagrams of self-organizing maps. Phys. Rev. E 54: 2807-2810.
• Riesenhuber, M., Bauer, H.-U., & Geisel, T. (1996). Analyzing phase transitions in high-dimensional self-organizing maps. Biol. Cyb. 75: 397-407.

Book Chapters

• 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.
• 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.
• 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.

Technical Reports

• Cadieu, C., Kouh, M., Riesenhuber, M., & Poggio, T. (2004). Shape Representation in V4: Investigating Position-Specific Tuning for Boundary Conformation with the Standard Model of Object Recognition. CBCL Paper #241/AI Memo #2004‒024, Massachusetts Institute of Technology, Cambridge, MA, November 2004.
• Serre, T., & Riesenhuber, M. (2004). Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex. CBCL Paper #239/AI Memo #2004‒004, Massachusetts Institute of Technology, Cambridge, MA, July 2004.
• Schneider, R., & Riesenhuber, M. (2004). On the Difficulty of Feature-based Attentional Modulations in Visual Object Recognition: A Modeling Study. CBCL Paper #235/AI Memo #2004‒004, Massachusetts Institute of Technology, Cambridge, MA, February 2004.
• Kouh,M. & Riesenhuber, M. (2003). Investigating Shape Representation in Area V4 with HMAX: Orientation and Grating Selectivities. CBCL Paper #231/AIM #2003‒021, Massachusetts Institute of Technology, Cambridge, MA, September 2003.
• Schneider, R., & Riesenhuber, M. (2003). A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition. CBCL Paper #218/AI Memo #2002‒011, Massachusetts Institute of Technology, Cambridge, MA, August 2002.
• Knoblich, U., Freedman, D.J., Riesenhuber, M. (2002). Categorization in IT and PFC: Model and Experiments. CBCL Paper #216/AI Memo #2002‒007, Massachusetts Institute of Technology, Cambridge, MA, April 2002.
• Knoblich, U. & Riesenhuber, M. (2002). Stimulus Simplification and Object Representation: A Modeling Study. CBCL Paper #215/AI Memo #2002‒004, Massachusetts Institute of Technology, Cambridge, MA, March 2002.
• Riesenhuber, M. Generalization Over Contrast and Mirror Reversal, but Not Figure-ground Reversal, in an "Edge-based" Model of IT Neurons. CBCL Paper #211/AI Memo #2001‒034, Massachusetts Institute of Technology, Cambridge, MA, December 2001.
• Riesenhuber, M., & Poggio, T. (2000). The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Object Classes. AI Memo 1682, CBCL Paper 185, Artificial Intelligence Lab and Center for Biological & Computational Learning, Massachusetts Institute of Technology.
• Riesenhuber, M., & Poggio, T. (1999). A Note on Object Class Representation and Categorical Perception. AI Memo 1679, CBCL Paper 183, Artificial Intelligence Lab and Center for Biological & Computational Learning, Massachusetts Institute of Technology.
• Riesenhuber, M., & Poggio, T. (1998). Modeling Invariances in Inferotemporal Cell Tuning. AI Memo 1629, CBCL Paper 160, Artificial Intelligence Lab and Center for Biological & Computational Learning, Massachusetts Institute of Technology.

Conference Papers

(selection, not up-to-date)

• Knoblich, U., Riesenhuber, M., Freedman, D.J., Miller, E.K., & Poggio, T. (2002). Visual Categorization: How the Monkey Brain Does It. In Biologically Motivated Computer Vision, Lee, S-W., H.H. Buelthoff & T. Poggio (eds.), Second IEEE International Workshop, BMCV 2002, Tuebingen, Germany, December 2002, 273-281.
• Serre, T., Riesenhuber, M., Louie, J., & Poggio, T. (2002). On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision. In Biologically Motivated Computer Vision, Lee, S-W., H.H. Buelthoff & T. Poggio (eds.), Second IEEE International Workshop, BMCV 2002, Tuebingen, Germany, December 2002, 387-397.
• Walther, D., Itti, L., Riesenhuber, M., Poggio, T., & Koch, C. (2002). Attentional Selection for Object Recognition - A Gentle Way. In Biologically Motivated Computer Vision, Lee, S-W., H.H. Buelthoff & T. Poggio (eds.), Second IEEE International Workshop, BMCV 2002, Tuebingen, Germany, December 2002, 472-479.
• Riesenhuber, M., & Poggio, T. (2000). CBF: A New Framework for Object Categorization in Cortex. In Biologically Motivated Computer Vision, Lee, S-W., H.H. Buelthoff & T. Poggio (eds.), First IEEE International Workshop, BMCV 2000, Seoul, Korea, May 2000.
• Riesenhuber, M., & Poggio, T. (1998). Just One View: Invariances in Inferotemporal Cell Tuning. In Advances in Neural Information Processing Systems 10, 215-221. MIT Press.
• Riesenhuber, M., & Poggio, T. (1997). Common Computational Strategies in Machine and Biological Vision. Proceedings of International Symposium on System Life, Tokyo, Japan, 67-75.
• Riesenhuber, M., & Dayan, P. (1997). Neural models for part-whole hierarchies. In Advances in Neural Information Processing Systems, 9, 17-23. MIT Press.
• Riesenhuber, M., Bauer, H.-U., & Geisel, T. (1997). On-center and off-center cell competition generates oriented receptive fields from non-oriented stimuli in Kohonen's self-organizing map. In Computational Neuroscience: Trends in Research, 1997, 471-476. Plenum.
• Bauer, H.-U., Riesenhuber, M., & Geisel, T. (1997). Calculating conditions for the emergence of structure in self-organizing maps. In Computational Neuroscience: Trends in Research, 1997, 247-252.
• Riesenhuber, M., Bauer, H.-U., & Geisel, T. (1996). Analyzing the formation of structure in high-dimensional self-organizing maps reveals differences to feature map models. In Artificial Neural Networks - ICANN 96, volume 1112 of Lecture Notes in Computer Science, 409-414. Springer.
• Sugiura A., Riesenhuber, M., & Koseki, Y. (1994). Comprehensibility improvement of tabular knowledge bases. In Proceedings of the Eleventh National Conference on Artificial Intelligence, 716-721. The AAAI Press / The MIT Press.

Department of Neuroscience | Georgetown University | Medical Center

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3970 Reservoir Rd NW, Washington, DC 20007

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