F A C U L T Y P R O F I L E
QIAN, NING, Ph.D.
Computational modeling of visual information processing; visual psychophysics.
Office: P.I. Annex | 5th floor | Room 519
Telephone: 212.543.6931, ext. 600
While experimental neurobiology has been making tremendous progress in our understanding of how the brain works, it has also been recognized that computational modeling is an indispensable tool of neuroscientific research. The building of quantitative models not only allows us to better understand the behavior of complex systems with many interacting components, but also helps us to generate new hypotheses and to design new experiments based on the predictions of the models. Computational methods are particularly important when higher brain functions such as visual perception and motor planning are being studied. An understanding of these functions requires not only experimental explorations, but also a computational theory specifying how the information carried by a large number of electrical and chemical signals in the brain can be combined to accomplish a given task.
The research in our laboratory is focused on vision modeling and psychophysics. The goal of our modeling work is to understand how a population of cortical cells with known physiological properties can act in concert to solve various vision problems, and to quantitatively explain our perceptual behavior. For example, we have shown recently that our perception of stereoscopic depth can be computed efficiently by a group of cells with properties similar to those found in the visual cortex. We have further demonstrated that this stereo model can be combined naturally with motion models into a unified framework. The unified model is not only physiologically plausible but can also explain many interesting behavioral observations such as a class of Pulfrich depth illusions and the structure-from-motion phenomenon. We are currently extending our model to an array of related vision problems, and testing the model predictions through new psychophysical experiments. In addition to motion and stereovision, we are also interested in visual perceptual learning, sensorimotor transformation, and engineering implementations of physiological models.
Qian, N., and Freeman, R.D. (2009) Pulfrich phenomena are coded effectively by a joint motion-disparity process. J Vision /9/(5):24, 1-16.
Xu, H., Dayan, P., Lipkin, R.M., and Qian, N. (2008). Adaptation across the cortical hierarchy: low-level curve adaptation affects high-level facial-expression judgments. J Neurosci/ 28/, 3374-3383.
Assee, A., and Qian, N. (2007). Solving da Vinci stereopsis with
depth-edge-selective V2 cells. Vision Res/ 47/, 2585-2602.
Tanaka, H., Krakauer, J.W., and Qian, N. (2006). An optimization
principle for determining movement duration. J Neurophysiol/ 95/, 3875-3886.
Chen, Y., and Qian, N. (2004). A coarse-to-fine disparity energy model
with both phase-shift and position-shift receptive field mechanisms.
Neural Comput/ 16/, 1545-1577.
Teich, A.F., and Qian, N. (2003). Learning and adaptation in a recurrent model of V1 orientation selectivity. J Neurophysiol/ 89/, 2086-2100.