Xin Huang

Department of Neuroscience Associate Professor Lab Website

Room 5505, Wisconsin Institutes for Medical Research (WIMR2)
1111 Highland Ave, Madison, WI 53705


B.S. in Biomedical Engineering and M.S. in Biophysics
Ph.D. in Neuroscience, Brown University
Postdoc in Systems Neuroscience, Salk Institute and UCSF

Neural basis of vision and visually guided behavior

Our laboratory seeks to understand the neural mechanisms underlying visual perception and visually guided behavior. Visual information is represented and processed by a large number of neurons distributed across dozens of brain areas, structured in a hierarchical, recurrent, and parallel manner. Each of these neurons is sensitive to certain features of the visual scene and has a constrained “view” of the world through its receptive field. As information flows deeper into the brain, neural representations transform from pixel-based to object-based, and from sensory to more cognitive and premotor – reflecting the processes of attention, decision-making, and motor planning.
The research in our laboratory aims to understand the following questions:

  • The neural processes underlying perceptual organization.
  • Transformation of information along the visual hierarchy.
  • Functional roles of massive and widespread feedback connections in the visual system.
  • Neural mechanisms underlying selective attention.
  • The principles and rationale of population neural coding.
  • Neural circuit mechanisms underlying canonical neural computations.

To investigate these questions, we use integrated approaches of modern electrophysiology, psychophysics, computation and neural network modeling, and behavioral tasks involving discrimination, attention, and decision-making. We are also developing and applying calcium imaging and optogenetic methods for circuit interrogation and manipulation.

Temporal dynamics of motion-based image segmentation. In this image, a river runs on the surface of a planet and bifurcates into two rivers as they flow to their distant destinations. The stars mimic the random-dot stimuli used in the study. The image illustrates the temporal dynamics of a sub-population of neurons in the visual cortex, as they encode two random-dot stimuli moving transparently in two directions separated by 60°. The neuronal response evolves from initially representing the vector average direction of the two stimulus components, to later representing the motion directions of the individual stimulus components. Artistic design and rendering by Xiaofeng Zhu. For more information, see the article by Xiao and Huang (2015).

Xin Huang

Areas of Expertise

  • Computational Biology & Bioinformatics
  • Neuroscience