Hyunjune Sebastian Seung
Systems and Circuits ; Theory and Computation
Systems and Circuits ; Theory and Computation

Professor of Computer Science and the Princeton Neuroscience Institute



sseung@princeton.edu
Research Lab
609-258-7713
153 Neuroscience


Research Focus

Sebastian Seung uses techniques from machine learning and social computing to extract brain structure from light and electron microscopic images. EyeWire showcases his approach by mobilizing gamers from around the world to create 3D reconstructions of neurons by interacting with a deep convolutional network. After reconstructing neural circuits, Seung applies computational methods to explain how they function. Current areas of interest include retinal, oculomotor, and cortical circuits.
Read more about Professor Seung.


Selected Publications

  • Ragan, T, Kadiri LR, Sutin J, Taranda J, Arganda-Carreras I, Kim Y, Kim Y, Seung SH, Osten P. 2012. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nature Methods.
  • Wickersham, IR, Sullivan HA, Seung HS. 2010. Production of glycoprotein-deleted rabies viruses for monosynaptic tracing and high-level gene expression in neurons. Nature Protocols. 5(3):595-606.
  • Wang, J, Hasan MT, Seung SH . 2009. Laser-evoked synaptic transmission in cultured hippocampal neurons expressing channelrhodopsin-2 delivered by adeno-associated virus. J Neurosci Methods. 183:165–75.
  • Turaga, SC, Briggman KL, Helmstaedter M, Denk W, Seung SH. 2009. Maximin affinity learning of image segmentation. CoRR. abs/0911.5372.
  • S. C. Turaga, J. F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, and H. S. Seung. Co
  • Y. Loewenstein, D. Prelec, and H. S. Seung. Operant matching as a Nash equilibrium of an intertemporal game. Neural Computation 21, 2755-2773 (2009).
  • H. S. Seung. Reading the Book of Memory: Sparse Sampling versus Dense Mapping of Connectomes. Neuron 62, 17-29 (2009).
  • V. Jain and H. S. Seung. Natural Image Denoising with Convolutional Networks. Advances in Neural Info. Proc. Systems 21 (Proceedings of NIPS '08), pp. 769-776. Cambridge, MA: MIT Press, 2009.
  • V. Jain, J. F. Murray, F. Roth, S. Turaga, V. Zhigulin, K. L. Briggman, M. N. Helmstaedter, W. Denk, and H. S. Seung. Supervised Learning of Image Restoration with Convolutional Networks. Proceedings: IEEE 11th International Conference on Computer Vision (ICCV) (2007).
  • C. Fang-Yen, M. C. Chu, H. S. Seung, R. R. Dasari, and M. S. Feld. Phase-referenced probe interferometer for biological surface profiling and displacement measurements. Rev. Sci. Instrum. 78, 123703 (2007).
  • I. R. Fiete, M. S. Fee, and H. S. Seung. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. J. Neurophysiol. 98, 2038-57 (2007; Epub 2007 Jul 25).
  • C. Fang-Yen, S. Oh, Y. Park, W. Choi, S. Song, H. S. Seung, R. R. Dasari, and M. S. Feld. Imaging voltage-dependent cell motions with heterodyne Mach-Zehnder phase microscopy. Opt. Lett. 32, 1572-4 (2007).
  • U. Rokni, A. G. Richardson, E. Bizzi, and H. S. Seung. Motor learning with unstable neural representations. Neuron 54, 65366 (2007).
  • D. Z. Jin, F. M. Ramazanoglu, and H. S. Seung. Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC. J. Comput. Neurosci. 23, 283-299 (2007; Epub 2007 Apr 18).

For a more complete list, see the attached file below.


Attached Files: