Publications

Found 146 results
Author [ Title(Desc)] Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
Shamir M, Sompolinsky H.  2004.  Nonlinear population codes. Neural Computation. 16:1105–1136.
Kim JW, Sompolinsky H.  1996.  On-line Gibbs learning. Physical Review Letters. 76:3021–3024.
Sompolinsky H, Kim J..  1998.  On-line Gibbs learning. I. General Theory.. Physical Review E, 58: 2335-2347..
Sompolinsky H, Kim J..  1998.  On-line Gibbs Learning. II. Application to Perceptron and multilayer networks.. Physical Review E, 58:2348-2362..
Sompolinsky H, Barkai N., Seung HS.  1995.  On-line Learning of Dichotomies: Algorithms and Learning Curves.. in Advances in Neural Information Processing Systems. Cowan J.D., Tesauro G. and Alspector J., editors, 7..
Loewenstein Y, Sompolinsky H.  2002.  Oscillations by symmetry breaking in homogeneous networks with electrical coupling. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 65:051926.
Misha T, Mitkov I, Sompolinsky H.  1993.  Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions. Physical Review Letters. 71:1280–1283.
Misha T, Mitkov I, Sompolinsky H.  1993.  Pattern of Synchrony in Integrate-and-Fire-Networks. In Proceedings of International Conference on Artificial Neural Networks, p. 622-627..
Goldberg JA, Rokni U, Sompolinsky H.  2004.  Patterns of ongoing activity and the functional architecture of the primary visual cortex. Neuron. 42:489–500.
Sompolinsky H, , .  1991.  Phase Coherence and Computation in a Neural Network of Coupled Oscillators. Non-Linear Dynamics and Neural Networks, Schuster H.G and Singer W, Eds. (VCH, Weinheim, 1991), pp. 113-140..
Kotliar G, Sompolinsky H.  1984.  Phase Transition in a Szyaloshinksy-Moriya Spin Glass.. Physical Review Letters 53, 1751..
Sompolinsky H, Yoon H, Kang K, Shamir M.  2001.  Population coding in neuronal systems with correlated noise. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 64:051904.
Tsodyks M., Sompolinsky H.  1992.  Processing of Sensory Information by a Network of Oscillators with Memory.. International Journal of Neural Systems 3, 51..
Barkai E, Kanter I, Sompolinsky H.  1990.  Properties of sparsely connected excitatory neural networks. Physical Review. A. 41:590–597.
Seung HS, Opper M.A., Sompolinsky H.  1992.  Query by Committee.. in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Warmuth M.K. and Valiant L.G., editors. (Kaufmann, San Mateo, CA), p. 287..

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