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Haim SompolinskyProfessor of PhysicsThe Neurophysics Lab, Racah Institute of Physics. Classical Electrodynamics-77401 Statistical Physics of Learning and Memory-77640 Introduction to Dynamics and Stochastic Processes-76931 Introduction to Analytical and Computational Methods in Brain Sciences-76930 Computational Neuroscience-Harvard (MCB 131)
Previous Neural Computation II Phase Transitions and Critical Phenomena The
Physics of Movement Location:
Recent Publications 2009
Burak Y, Lewallen S, Sompolinsky H (2009) Stimulus-dependent correlations in threshold-crossing spiking neurons, Neural Computation, 2009 Aug;21 (8):2269-308 (pdf) Gütig R. and Sompolinsky H., Time-Warp-Invariant Neuronal Processing. PLoS Biology 7 e1000141 (doi:10.1371/journal.pbio.1000141) (pdf) (Figure S1 TIF) (Table S1 PDF) (Text S1 PDF) 2007-2008
Ganguli S., Huh D., Sompolinsky H., 2008. Memory traces in dynamical systems. 18970 - 18975 | PNAS | December 2, 2008 | vol. 105 | no. 48 (pdf) (Supplement) Ilana B. Witten, Eric I. Knudsen and Haim Sompolinsky. A Hebbian Learning Rule Mediates Asymmetric Plasticity in Aligning Sensory Representations. J Neurophysiol 100: 1067-1079, 2008. First published June 4, 2008; doi:10.1152/jn.00013.2008 (pdf) Pitkow X., Sompolinsky H., and Meister M. 2007. A neural computation for visual acuity in the presence of eye movements. PLoS Biol. December 2007 | Volume 5 | Issue 12 | e331 (pdf) Safran M, Flanagin V., Borst A., and Sompolinsky H., 2007. Adaptation and information transmission in fly motion detection. J. Neurophysiol. 98(6):3309-20. (pdf) 2004-2006
Gutig R., Sompolinsky H. The tempotron: a neuron that learns spike timing-based decision. Nature Neuroscience. 9(3):420-428 (2006) (pdf) Shamir M, Sompolinsky H. Implications of Neuronal Diversity on Population Coding. Neural Computation. 18 (8): 1951-1986 (2006). (pdf) Leowenstein Y., Mahon S., Chadderton P., Kitamura K., Sompolinsky H., Yarom Y. and Häusser M., Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nature Neuroscience 8(2):202-11. (2005) (pdf) (Supplement) Borst A, Flanagin VL, and Sompolinsky H. Adaptation without parameter change: dynamic gain control in Reichardt motion detectors. Proceedings of the National Academy of Science (USA), 102(17):6172-6. (2005) (pdf) Shamir M. and Sompolinsky H. Nonlinear population codes. Neural Computation 16: 1105-1136. (2004) (pdf) Goldberg JA, Rokni U, and Sompolinsky H. Patterns of ongoing activity and the functional architecture of the primary visual cortex. Neuron, 42: 489-500. (2004) (pdf) White O., Lee D. and Sompolinsky H.Short term memory in orthogonal neural networks. Physical Review Letters, 9:148102. (2004) (pdf) Kang K., Shapely R., and Sompolinsky H. Information tuning of populations of neurons in primary visual cortex. Journal of Neuroscience, 24 (15):3726-3735. (2004) (pdf) 2001-2003 
Rokni U., Steinberg O., Vaadia E., and Sompolinsky H. Cortical representation of bimanual movements. Journal ofNeuroscience, 23 (37):11577-11586. (2003) (pdf) Shriki O., Hansel D. and Sompolinsky H. Rate models for conductance-based cortical neuronal networks. Neural Computation, 15(8):1809-1841. (2003) (pdf) Loewenstein Y, and Sompolinsky H., Temporal integration by calcium dynamics in a model neuron. Nature Neuroscience, 6(9): 961-967.(2003) (pdf) (Supplementary information) Gütig R, Aharonov R, Rotter S, and Sompolinsky H Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity. Journal of Neuroscience, 23(9):3697-3714.(2003) (pdf) Litvak V, Sompolinsky H, Segev I, and Abeles M., On the Transmission of Rate Code in Long Feedforward Networks with Excitatory-Inhibitory Balance Journal of Neuroscience, 23(7):3006-3015. (2003) (pdf) Kang K., Shelley M. and Sompolinsky H. Mexican hats and pinwheels in visual cortex. Proceedings of the National Academy of Sciences USA, 100: 2848-2853. (2003) (pdf) Loewenstein Y, and Sompolinsky H Oscillations by symmetry breaking in homogeneous networks with electrical coupling. Physical Review E, 65: 1-11.(2002) (pdf) Shamir M and Sompolinsky H. Correlation Codes in Neuronal Networks. In Advances in Neural Information Processing Systems 14.(2001) (ps) (pdf) Loewenstein Y, Yarom Y and Sompolinsky H. The Generation of Oscillations in Networks of Electrically Coupled Cells. Proceedings of National Academy of Science (USA), Vol. 98, No. 14: 8095-8100.(2001) (pdf) Rubin J, Lee D, and Sompolinsky H. The Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity. Physical Review Letters 86: 364.(2001) (ps) (pdf) (zip) Sompolinsky H, Yoon H, Kang K, and Shamir M. Population Coding in Neuronal Systems with Correlated Noise. Physical Review E, 64:051904. (2001) (ps) (pdf) Kang K and Sompolinsky H (2001) Mutual Information of Population Codes and Distance Measures in Probability Space. Physical Review Letters, 86: 4958-4961 (ps) (pdf) Shriki O., Sompolinsky H. and Lee D.D, (2001) An Information Maximization Approach to Overcomplete and Recurrent Representations. In Advances in Neural Information Processing Systems 13, Leen T.K, Dietterich T.G, and Tresp V, Eds. 13:612-618 (ps) (pdf) (zip) Ahronov-Barki R, Gutig R, Rotter S, Aertsen A, and Sompolinsky H. (2001) The Generalized Synaptic Updating in Temporally Asymmetric Hebbian Learning. CNS (ps) (pdf)
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