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Gütig R, Aharonov R, Rotter S., Sompolinsky H.  2003.  Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 23:3697–3714.PDF icon gutig-somp-03.pdf (542.64 KB)
Gütig R, Sompolinsky H.  2006.  The tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience. 9:420–428.PDF icon robert-sompo-06.pdf (536.99 KB)
Gütig R, Sompolinsky H.  2009.  Time-warp-invariant neuronal processing. PLoS Biology. 7:e1000141.PDF icon guetig_sompolinsky09_text_s1.pdf (27.89 KB)PDF icon guetig_sompolinsky09_table_s1.pdf (9.27 KB)PDF icon guetig_sompolinsky09.pdf (1.81 MB)Image icon guetig_sompolinsky09_figure_s1.png (136.16 KB)
Gross DJ, Kanter I, Sompolinsky H.  1985.  Mean-field theory of the Potts glass. Physical Review Letters. 55:304–307.PDF icon PhysRevLett.55.304.pdf (211.13 KB)
Grannan E.R., Sompolinsky H, Kleinfeld D.  1993.  Stimulus Dependent Synchronization of Neuronal Assemblies.. Neural Computation 5, 550..
Golomb D, Rubin N, Sompolinsky H.  1990.  Willshaw model: Associative memory with sparse coding and low firing rates. Physical Review. A. 41:1843–1854.PDF icon PhysRevA.41.1843.pdf (627.63 KB)
Golomb D, Hansel D, Shraiman B, Sompolinsky H.  1992.  Clustering in globally coupled phase oscillators. Physical Review. A. 45:3516–3530.PDF icon PhysRevA.45.3516.pdf (745.69 KB)
Goldberg JA, Rokni U, Sompolinsky H.  2004.  Patterns of ongoing activity and the functional architecture of the primary visual cortex. Neuron. 42:489–500.PDF icon goldberg-somp-04.pdf (331.46 KB)
Gjorgjieva J, Sompolinsky H, Meister M.  2014.  Benefits of pathway splitting in sensory coding. PDF icon Benefits of pathway splitting in sensory coding.pdf (1.71 MB)
Ginzburg I., Sompolinsky H.  1994.  Correlation Functions in a Large Stochastic Neural Network.. in Advances in Neural Information Processing Systems. Cowan J.D., Tesauro G. and Alspector J., editors, 6, 471-476..
Ginzburg II, Sompolinsky H.  1994.  Theory of correlations in stochastic neural networks. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 50:3171–3191.
Ganguli S, Sompolinsky H.  2010.  Short-term memory in neuronal networks through dynamical compressed sensing. Neural Information Processing Systems. PDF icon 3980-short-term-memory-in-neuronal-networks-through-dynamical-compressed-sensing.pdf (532.26 KB)
Ganguli S, Sompolinsky H.  2010.  Statistical mechanics of compressed sensing. Physical Review Letters. 104:188701.PDF icon physrevlett.104.188701-supp.pdf (357.97 KB)PDF icon physrevlett.104.188701.pdf (288.8 KB)
Ganguli S, Sompolinsky H.  2012.  Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. Annual Review of Neuroscience. 35:485–508PDF icon sompolinsky_annurev-neuro-062111-150410.pdf (819.24 KB)
Ganguli S, Huh D, Sompolinsky H.  2008.  Memory traces in dynamical systems. Proceedings of the National Academy of Sciences(USA). 105:18970–18975.PDF icon 08.memory.pdf (543.81 KB)PDF icon 08.memoryS.pdf (14.92 KB)