Principal Investigator, Visual Neuroscience Group
The Rowland Institute at Harvard
Email: cox@rowland.harvard.edu
Curriculum Vitae | Lab Site
Research Interests [projects] [news]
We recognize visual objects with such ease that it is easy to overlook what an impressive computational feat this represents. Any given object in the world can cast an effectively infinite number of different images onto the retina, depending on its position relative to the viewer, the configuration of light sources, and the presence of other objects in the visual field. In spite of this extreme variation, biological visual systems are able to effortlessly recognize at least hundreds of thousands of distinct object classes—a feat that no current artificial system can come close to achieving.
My laboratory seeks to understand the computational underpinnings of object recognition through a concerted effort on two fronts. First, we endeavor to understand the workings of biological visual systems using a variety of experimental techniques, ranging from microelectrode recordings in living brains to visual psychophysics in humans. Second, we attempt to instantiate what we have learned into artificial object recognition systems, leveraging recent advances in parallel computing to build systems that begin to approach the scale of natural systems. By combining reverse- and forward-engineering approaches, we hope to accelerate progress in both domains.
Selected Publications [see more]
Pinto N, Doukhan D, DiCarlo JJ, Cox DD (2009) A High-Throughput Screening Approach to Discovering Good Forms of Biologically-Inspired Visual Representation. PLoS Computational Biology. 5(11): e1000579. doi:10.1371/journal.pcbi.1000579. [link] [researchcast] [Science editor’s choice]
Zoccolan D, Oertelt N, DiCarlo JJ, Cox DD (2009) A rodent model for the study of invariant visual object recognition. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.0811583106 [link]
Pinto N, DiCarlo JJ, Cox DD (2009) How far can you get with a modern face recognition test set using only simple features? IEEE Computer Vision and Pattern Recognition [link]
Pinto N, Dicarlo JJ, Cox DD (2008) Establishing Benchmarks and Baselines for Face Recognition. Proc. ECCV Faces in Real Life Images, oai:hal.inria.fr:inria-00326721_v1 [link]
Cox DD, Papanastassiou A, Oreper D, Andken B, DiCarlo JJ (2008) High-Resolution Three-Dimensional Microelectrode Brain Mapping Using Stereo Microfocal X-Ray Imaging. Journal of Neurophysiology, doi:10.1152/jn.90672.2008 [link]
DiCarlo JJ and Cox DD. (2007) Untangling invariant object recognition. Trends in Cognitive Science 11:333-341 [link]
Cox DD, Meier P, Oertelt N, DiCarlo JJ (2005) 'Breaking' Position-Invariant Object Recognition. Nature Neuroscience, 8: 1145-1147. [link]
Cox D, Meyers E, Sinha P (2004) Contextually Evoked Object-Specific Responses in Human Visual Cortex. Science 304:115-117 [link]
Cox DD & Savoy RL (2003) Functional Magnetic Resonance Imaging (fMRI) "Brain Reading:" Detecting and Classifying Distributed Patterns of fMRI Activity in Human Visual Cortex. Neuroimage 19: 261-270. [link]