Sebastian Seung



  • Advisor
  • Learning in Machines & Brains


  • Princeton University
Princeton Neuroscience Institute


  • United States


PhD (Theoretical Physics), Harvard University


Sebastian Seung is a computational neuroscientist whose laboratory uses techniques from machine learning and social computing to extract brain structure from light and electron microscopic images.

The citizen science project, EyeWire, showcases their approach by mobilizing gamers from around the world to create 3D reconstructions of neurons by interacting with a deep convolutional network.


Wall Street Journal Top 10 Nonfiction

Ho-Am Prize in Engineering (Ho-Am Foundation, Korea)

McKnight Endowment Fund for Neuroscience Scholar Award

David and Lucile Packard Fellowship

Sloan Research Fellow

Relevant Publications

Kim, Y. et al. "Mapping social behavior-induced brain activation at cellular resolution in the mouse." Cell Rep. 10, no. 2 (January 2015): 1–14.

Sümbül, U. et al. "Automated computation of arbor densities: a step toward identifying neuronal cell types." Front. Neuroanat. 8, no. 139 (November 2014).

Seung, H.S., and U. Sümbül. "Neuronal cell types and connectivity: lessons from the retina." Neuron 83, no. 6 (September 2014): 1262–1272.

Kim, J.S. et al. "Space-time wiring specificity supports direction selectivity in the retina." Nature 509, no. 7500 (May 2014): 331–36.

Seung, S. Connectome: How the Brain’s Wiring Makes Us Who We Are. Boston: Houghton Mifflin Harcourt, 2012.


Seung Lab