Publications
] Filters: First Letter Of Last Name is C [Clear All Filters]
[cox_breaking_2005] "'Breaking' position-invariant object recognition." Nature Neuroscience. 2005;8:1145-1147. Abstract
[cox_does_2008] "Does learned shape selectivity in inferior temporal cortex automatically generalize across retinal position?" The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2008;28:10045-10055. Abstract
[cox_high-resolution_2008] "High-resolution three-dimensional microelectrode brain mapping using stereo microfocal X-ray imaging." Journal of Neurophysiology. 2008;100:2966-2976. Abstract
[pinto_high-throughput_2009] "A high-throughput screening approach to discovering good forms of biologically inspired visual representation." {PLoS} Computational Biology. 2009;5:e1000579. Abstract
[zoccolan_multiple_2005] "Multiple object response normalization in monkey inferotemporal cortex." The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2005;25:8150-8164. Abstract
[zoccolan_rodent_2009] "A rodent model for the study of invariant visual object recognition." Proceedings of the National Academy of Sciences of the United States of America. 2009;106:8748-8753. Abstract
[dicarlo_untangling_2007] "Untangling invariant object recognition." Trends in Cognitive Sciences. 2007;11:333-341. Abstract
[li_what_2009] "What response properties do individual neurons need to underlie position and clutter "invariant" object recognition?" Journal of Neurophysiology. 2009;102:360-376. Abstract
[pinto_why_2008] "Why is real-world visual object recognition hard?" {PLoS} Computational Biology. 2008;4:e27. Abstract
[52] Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks. Kona, HI; 2011.
[48] The effect of visual experience on the position tolerance of primate object representations. San Diego, CA; 2004.
[70] A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Snowbird, UT; 2010.
[83] A high-throughput screening approach to discovering good forms of visual representation. Salt Lake City, UT; 2008.
[69] Human versus machine: comparing visual object recognition systems on a level playing field. Snowbird, UT; 2010.
[85] Is the rodent a valuable model system for studying invariant object recognition?. Salt Lake City, UT; 2008.
[45] Is the “binding problem” a problem in inferiotemporal cortex?. Washington, DC; 2005.
[46] Multiple object response normalization in monkey inferotemporal cortex. Washington, DC; 2005.
[74] Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. San Jose, CA; 2009.
[72] Unlocking Brain-Inspired Computer Vision. Boston University, MA; 2009.
[73] The Visual Cortex and GPUs. MGH Boston, MA; 2009.
[84] Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition. Salt Lake City, UT; 2008.
[42] "Neural dynamics of hippocampal modulation of classical conditioning." In: Commons M, Grossberg S, Staddon JER, eds. Neural Network Models of Conditioning and Action. Hillsdale, NJ: Lawrence Erlbaum Association; 1991.
