Sebastian Seung Computational neuroscientist
The Seung Lab 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 Ten Nonfiction, 2012.
The Ho-Am Prize in Engineering (Ho-Am Foundation, Korea), 2008.
McKnight Endowment Fund for Neuroscience Scholar Award, 2001.
David and Lucile Packard Fellowship, David and Lucile Packard Fellowship.
Sloan Research Fellow, 1999.
Y. Kim et al, "Mapping social behavior-induced brain activation at cellular resolution in the mouse," Cell Rep., vol. 10, no. 2, pp. 1-14, Jan. 2015.
U. Sümbül et al, "Automated computation of arbor densities: a step toward identifying neuronal cell types," Front. Neuroanat., vol. 8, no. 139, Nov. 2014.
H.S. Seung and U. Sümbül, "Neuronal cell types and connectivity: lessons from the retina," Neuron, vol. 83, no. 6, pp. 1262-1272, Sept. 2014.
J. S. Kim et al, "Space-time wiring specificity supports direction selectivity in the retina," Nature, vol. 509, no. 7500, pp. 331-336, May 2014.
S. Seung, Connectome: How the Brain’s Wiring Makes Us Who We Are. Boston: Houghton Mifflin Harcourt, 2012.
Advisor Learning in Machines & Brains
Princeton UniversityDepartment of Computer Science
PhD (Theoretical physics) Harvard University
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Sebastian Seung talks about his latest book, Connectome