Publications of Sebastian Seung

Operant matching/melioration

• Y. Loewenstein and H. S. Seung. Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity. (pdf) Proc. Natl. Acad. Sci. USA 103, 15224-9 (2006).
• G. S. Corrado, L. P. Sugrue, H. S. Seung, and W. T. Newsome. Linear-nonlinear-Poisson models of primate choice dynamics. (pdf) J. Exp. Anal. Behav. 84, 581-617 (2005).

Synaptic basis of reinforcement learning

• I. R. Fiete and H. S. Seung. Gradient learning in spiking neural networks by dynamic perturbation of conductances. (pdf) Phys. Rev. Lett. 97, 048104 (2006).
  
• J. Werfel, X. Xie, and H. S. Seung. Learning curves for stochastic gradient descent in linear feedforward networks. (pdf) Neural Comput. 17, 2699-2718 (2005). Earlier version:  J. Werfel, X. Xie, and H. S. Seung. Learning curves for stochastic gradient descent in linear feedforward networks. (pdf) Adv. Neural Info. Proc. Syst. 16 (2004).
• X. Xie and H. S. Seung. Learning in neural networks by reinforcement of irregular spiking. (pdf) Phys. Rev. E69, 041909 (2004).
• H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. (pdf) Neuron 40, 1063-1073 (2003). 

Nonnegative matrix factorization

• D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. (pdf) Adv. Neural Info. Proc. Syst. 13, 556-562 (2001).
• D. D. Lee and H. S. Seung.  Learning the parts of objects by non-negative matrix factorization. (pdf) Nature 401, 788-791 (1999).
  • B. W. Mel. Computational neuroscience: Think positive to find parts (pdf), News and Views, Nature, 401, 759-760 (1999).
• D. D. Lee and H. S. Seung.  Unsupervised learning by convex and conic coding. (pdf) Adv. Neural Info. Proc. Syst. 9, 515-521 (1997).

Neural stimulation and recording methods

• A. Starovoytov, J. Choi, and H. S. Seung. Light-directed electrical stimulation of neurons cultured on silicon wafers. (pdf) J. Neurophysiol. 93, 1090-1098 (2005; Epub 2004 Sep 22).
  
• C. Fang-Yen, M. C. Chu, H. S. Seung, R. R. Dasari, and M. S. Feld. Noncontact measurement of nerve displacement during action potential with a dual-beam low-coherence interferometer. (pdf) Opt. Lett. 29, 2028-2030 (2004).

Top-down and bottom-up processing in networks

• V. Jain, V. Zhigulin, and H. S. Seung. Representing part-whole relationships in recurrent neural networks. (pdf) Adv. Neural Info. Proc. Syst. 18, 563--70 (2006).
• J.-H. Oh and H. S. Seung.  Learning generative models with the up-propagation algorithm.(pdf) Adv. Neural Info. Proc. Syst. 10, 605-11 (1998).
• H. S. Seung.  Pattern analysis and synthesis in attractor neural networks. (pdf)   In Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective, Proceedings of TANC'97.   Springer-Verlag (1997).

Sequence generation and learning

• Dezhe Z. Jin, Fethi M. Ramazanoğlu, and H. Sebastian Seung. Intrinsic bursting enhances the robustness of a neural
  network model of sequence generation by avian brain area HVC. (pdf) J. Comput. Neurosci. DOI 10.1007/s10827-007-0032-z.
• I. R. Fiete, R. H. R. Hahnloser, M. S. Fee, and H. S. Seung. Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong. (pdf) J. Neurophysiol. 92, 2274-2282 (2004).

Neural network theory  

• H. S. Seung. Amplification, Attenuation, and Integration (pdf) in The Handbook of Brain Theory and Neural Networks: Second Edition (M. A. Arbib, Editor) Cambridge, MA: MIT Press, pp. 94-97 (2003).
• R. H. R. Hahnloser, H. S. Seung, and J. J. Slotine. Permitted and forbidden sets in symmetric threshold-linear networks. (pdf) Neural Comput. 15, 621-38 (2003). Earlier version: R. H. R. Hahnloser, H. S. Seung. Permitted and forbidden sets in symmetric threshold linear networks. (pdf) Adv. Neural Info. Proc. Syst. 13, 217-223 (2001).
• X. Xie and H. S. Seung. Equivalence of backpropagation and contrastive Hebbian learning in a layered network. (pdf) Neural Comput. 15, 441-54 (2003).
• X. Xie, R. H. R. Hahnloser, and H. S. Seung. Selectively grouping neurons in recurrent networks of lateral inhibition. (pdf) Neural Comput. 14, 2627-46 (2002). Earlier version: X.-H. Xie, R. Hahnloser and H. S. Seung. Learning winner-take-all competition between groups of neurons in lateral inhibitory networks (pdf) Adv. Neural Info. Proc. Syst. 13, 350-356 (2001).
• D. Z. Jin and H. S. Seung. Fast computation with spikes in a recurrent neural network. (pdf) Phys. Rev. E65, 051922 (2002).
• R. H. R. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, and H. S. Seung. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. (pdf) Nature 405, 947-51 (2000).
  
  • C. Dioro and R. P.N. Rao. Neural Circuits in Silicon (pdf), News and Views, Nature, 405, 891-2 (2000).
• H. S. Seung, T. J. Richardson, J. C. Lagarias, and J. J. Hopfield.  Minimax and Hamiltonian dynamics of excitatory-inhibitory networks.(pdf) Adv. Neural Info. Proc. Syst.10, 329-35 (1998).
• N. D. Socci, D. D. Lee, and H. S. Seung.  The rectified Gaussian distribution. (pdf) Adv. Neural Info. Proc. Syst.10, 350-6 (1998).

Continuous attractors

• H. S. Seung and D.D. Lee. The manifold ways of perception. (pdf) Science 290, 2268-69 (2000).
  
• H. S. Seung.  Learning continuous attractors in recurrent networks.(pdf)  Adv. Neural Info. Proc. Syst. 10, 654-60 (1998).
• H. S. Seung. Continuous attractors and oculomotor control. (pdf) Neural Networks 11, 1253-58 (1998).
  
• H. S. Seung.  How the brain keeps the eyes still. (pdf)  Proc. Natl. Acad. Sci. USA 93, 13339-44 (1996).
  

Persistent activity and neural integrators

• G. Major, R. Baker, E. Aksay, H. S. Seung, and D. W. Tank. Plasticity and tuning of the time course of analog persistent firing in a neural integrator. (pdf) Proc. Natl. Acad. Sci. USA 101, 7745-7750 (2004). 
• G. Major, R. Baker, E. Aksay, B. Mensh, H. S. Seung, and D. W. Tank. Plasticity and tuning by visual feedback of the stability of a neural integrator. (pdf) Proc. Natl. Acad. Sci. 101, 7739-7744 (2004). 
  
• B. D. Mensh, E. Aksay, D. D. Lee, H. S. Seung, and D. W. Tank. Spontaneous eye movements in goldfish: Oculomotor integrator performance, plasticity, and dependence on visual feedback. (pdf) Vision Res. 44, 711-726 (2004). 
• E. Aksay, R. Baker, H. S. Seung, and D. W. Tank. Correlated discharge among cell pairs within the oculomotor horizontal velocity-to-position integrator. (pdf) J. Neuroscience 23, 10852-10858 (2003).  
• M. S. Goldman, J. H. Levine, G. Major, D. W. Tank, and H. S. Seung. Robust Persistent Neural Activity in a Model Integrator with Multiple Hysteretic Dendrites per Neuron. (pdf) Cerebral Cortex 13, 1185-1195 (2003). 
• E. Aksay, G. Major, M. S. Goldman, R. Baker, H. S. Seung, and D. W. Tank. History dependence of rate covariation between neurons during persistent activity in an oculomotor integrator. (pdf) Cerebral Cortex 13, 1173-1184 (2003).
• X. Xie, R. H. R. Hahnloser, and H. S. Seung. Double-ring network modeling of the head-direction system (pdf) Phys. Rev. E66, 041902 (2002).
  
• M. S. Goldman, C. R. Kaneko, G. Major, E. Aksay, D. W. Tank, and H. S. Seung. Linear regression of eye velocity on eye position and head velocity suggests a common oculomotor neural integrator. (pdf) J. Neurophysiol. 88, 659-65 (2002).
• E. Aksay, G. Gamkrelidze, H. S. Seung, R. Baker and D. W. Tank. In vivo intracellular recording and perturbation of persistent activity in a neural integrator (pdf) Nature Neurosci. 4, 184- 93 (2001).
• H. S. Seung. Half a century of Hebb. (pdf) Nature Neurosci. 3, 1166-67 (2000).
• X.-H. Xie and H. S. Seung.  Spike-based learning rules and stabilization of persistent neural activity. (pdf)  Adv. Neural Info. Proc. Syst. 12, 199-205 (2000).
• E. Aksay, R. Baker, H. S. Seung, and D. W. Tank. Anatomy and Discharge Properties of Pre-Motor Neurons in the Goldfish Medulla That Have Eye-Position Signals During Fixations. (pdf) J. Neurophysiol. 84, 1035-49 (2000).
• H. S. Seung, D. D. Lee, B. Y. Reis, D. W. Tank. Stability of the memory of eye position in a recurrent network of conductance-based model neurons. (pdf) Neuron 26, 259-271 (2000).
• H. S. Seung, D. D. Lee, B. Y. Reis, D. W. Tank. The autapse: a simple illustration of short-term analog memory storage by tuned synaptic feedback. J. Comput. Neurosci. 9:171-85 (pdf)  (2000).
• D. D. Lee, B. Y. Reis, H. S. Seung, and D. W. Tank.  Nonlinear network models of the oculomotor integrator. (pdf).   In Computational Neuroscience: Trends in Research 1997.  New York, Plenum Press.
  

Learning and Robotics

• R. Tedrake, T. W. Zhang, and H. S. Seung. Learning to Walk in 20 Minutes. (pdf) In Proceedings of the Fourteenth Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, 2005.
• R. Tedrake, T. W. Zhang, and H. S. Seung. Stochastic Policy Gradient Reinforcement Learning on a Simple 3D Biped. (pdf) In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pages 2849-2854, Sendai, Japan, September 2004.
• R. Tedrake, T. W. Zhang, M.-F. Fong, and H. S. Seung. Actuating a Simple 3D Passive Dynamic Walker. (pdf) In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), volume 5, pages 4656-4661, New Orleans, LA, April 2004.
• R. Tedrake and H. S. Seung. Improved Dynamic Stability using Reinforcement Learning. (pdf) In 5th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR), pages 341-348, Paris, France, September 2002.
• A. Gelperin, J. L. Dawson, S. M. Cazares. and H. S. Seung.  Rapid fruit cultivar identification by an artificial olfactory system.  Proceedings of the 5th International Symposium on Olfaction and the Electronic Nose. 263-74 (1998).
• D. D. Lee and H. S. Seung. Learning in intelligent embedded systems. (pdf)
  
• D. D. Lee and H. S. Seung.  A neural network based head tracking system. (pdf) Adv. Neural Info. Proc. Syst.10, 908-14 (1998).

Analysis of neural activity data

• B. D. Mensh, J. Werfel, and H. S. Seung. BCI Competition 2003 - Data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. (pdf) IEEE Trans. Biomed. Eng. 51, 1052-1056 (2004).

Query learning

• Y. Freund, H. S. Seung, E. Shamir, and N. Tishby.  Selective sampling using the Query by Committee algorithm (pdf)  Machine Learning  28, 133-168 (1997).  Earlier version: Information, prediction, and query by committee.  Adv. Neural Info. Proc. Syst. 5, 483-490 (1993).
• H. S. Seung, M. Opper, and H. Sompolinsky.  Query by committee. (pdf)  In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, 287-94 (1992). 

Sensory neuroscience

• D. Z. Jin, V. Dragoi, M. Sur, and H. S. Seung. Tilt aftereffect and adaptation-induced changes in orientation tuning in visual cortex. (pdf) J. Neurophysiol. 94, 4038-4050 (2005).
• A. Borst, M. Egelhaaf, and H. S. Seung.  Two-dimensional motion perception in flies.  Neural Comput. 5, 856-868 (1993).
• H. S. Seung and H. Sompolinsky.  Simple models for reading neuronal population codes.  Proc. Natl. Acad. Sci. USA 90, 10749-53 (1993).
  

Learning curves  

• P. Riegler and H. S. Seung.  Vapnik-Chervonenkis entropy of the spherical perceptron. (pdf)  Phys. Rev. E55, 3283-7 (1997).
• D. Haussler, M. J. Kearns, H. S. Seung, and N. Tishby.  Rigorous learning curve bounds from statistical mechanics (pdf)  Machine Learning 25, 195-236 (1996) and Proceedings of the Seventh Annual ACM Workshop on Computational Learning Theory, 76-87 (1994). 
• H. S. Seung.  Annealed theories of learning. (pdf)  In Neural Networks: The Statistical Mechanics Perspective, Proceedings of the CTP-PBSRI Joint Workshop on Theoretical Physics, World Scientific, 32-41 (1995).
  
• H. Sompolinsky, N. Barkai, and H. S. Seung.  On-line learning of dichotomies: algorithms and learning curves.  In Neural Networks: The Statistical Mechanics Perspective, Proceedings of the CTP-PBSRI Joint Workshop on Theoretical Physics, World Scientific, 105-130 (1995). 
• N. Barkai, H. S. Seung, and H. Sompolinsky.  Local and global convergence of on-line learning.  Phys. Rev. Lett. 75, 1415-18 (1995). 
• N. Barkai, H. S. Seung, and H. Sompolinsky.  On-line learning of dichotomies. (pdf) Adv. Neural Info. Proc. Syst. 7, 303-310 (1995).
  
• M. Kearns and H. S. Seung.  Learning from a population of hypotheses. (pdf)  Machine Learning 18, 255-276 (1995) and Proceedings of the Sixth Annual Workshop on Computational Learning Theory, 101-110 (1993).
• N. Barkai, H. S. Seung, and H. Sompolinsky.  Scaling laws in learning of classification tasks.  Phys. Rev. Lett. 70, 3167-70 (1993). 
• H. S. Seung, H. Sompolinsky, and N. Tishby.  Statistical mechanics of learning from examples.  Phys. Rev. A45, 6056-91 (1992).   Earlier version: Learning curves in large neural networks. Proceedings of the Fourth Annual ACM Workshop on Computational Learning Theory. 112-126 (1991). 
• H. Sompolinsky, N. Tishby, and H. S. Seung.  Learning from examples in large neural networks.  Phys. Rev. Letters 65, 1683-6 (1990).

Condensed matter physics

• D. A. Huse and H. S. Seung.  Possible vortex-glass transition in a model random superconductor.  Phys. Rev. B42, 1059-61 (1990). 
• D. R. Nelson and H. S. Seung.  Theory of melted flux liquids.  Phys. Rev. B39, 9153-74 (1989).
  
• H. S. Seung and D. R. Nelson. Defects in flexible membranes with crystalline order.  Phys Rev. A38, 1005-18 (1988).