In humans, no less than one third of the brain is devoted to visual functions, hence, vision may be seen as a window to our brain. The abundance of experimental data on vision makes it a fertile ground for theoretical understanding. Nevertheless, to understand our brain using our own brains can be very challenging, as seen in many unexpected and counter-intuitive difficulties and failures encountered by generations of scientists. I will give a quick overview of the field of computational neuroscience, and then show a detailed example of how data, a network model, and a theory work together to understand the cognitive phenomenon of visual attention. For more details, see: http://iopscience.iop.org/article/10.1088/1478-3975/13/3/035002

Dr. Li obtained her B.S. in Physics in 1984 from Fudan University. In 1983, she took part in the CUSPEA program and was ranked 1st among 800 applicants nation-wide. She obtained her Ph.D. in Physics in 1989 from California Institute of Technology, under the guidance of Prof. John Hopfield. After working as a postdoc at the Fermi National Laboratory, Institute for Advanced Study at Princeton, and Rockefeller University, she joined the Computer Science Department at the Hong Kong University of Science and Technology in 1994. In 1998, she helped to found the Gatsby Computational Neuroscience Unit in University College London. Currently, she is a Professor of computational neuroscience in the Department of Computer Science in University College London. Her research spans from high energy physics to neurophysiology and marine biology, with most experience in understanding the brain functions in vision, olfaction, and in nonlinear neural dynamics. In late 90s and early 2000s, she proposed a theory (which is being extensively tested) that the primary visual cortex in the primate brain creates a saliency map to automatically attract visual attention to salient visual locations. She is the author of “Understanding Vision: theory, models, and data,” Oxford University Press, 2014.