Publications

Found 146 results
[ Author(Desc)] Title 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 
Litvak V, Sompolinsky H, Segev I, Abeles M.  2003.  On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 23:3006–3015.
Loewenstein Y, Mahon S, Chadderton P, Kitamura K, Sompolinsky H, Yarom Y, Häusser M.  2005.  Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nature Neuroscience. 8:202–211.
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.
Loewenstein Y, Sompolinsky H.  2003.  Temporal integration by calcium dynamics in a model neuron. Nature Neuroscience. 6:961–967.
Loewenstein Y, Yarom Y, Sompolinsky H.  2001.  The generation of oscillations in networks of electrically coupled cells. Proceedings of the National Academy of Sciences of the United States of America. 98:8095–8100.
Maoz S, Sompolinsky H.  2000.  Thouless-anderson-palmer equations for neural networks. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 61:1839–1844.
Mato G, Sompolinsky H.  1996.  Neural network models of perceptual learning of angle discrimination. Neural Computation. 8:270–299.
Memmesheimer R-M, Rubin R, Olveczky BP, Sompolinsky H.  2014.  Learning Precisely Timed Spikes. Neuron. 84(4)
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..
Rajan K, Abbott LF, Sompolinsky H.  2010.  Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. Neural Information Processing Systems.
Rajan K, Abbott LF, Sompolinsky H.  2010.  Stimulus-dependent suppression of chaos in recurrent neural networks. Physical Review. E,. 82:011903.
Ranit A-B.  1993.  Theoretical Issues in Learning from Examples. In Proceedings of the Third NEC Symposium on Computational Learning and Cognition..
Rokni U, Steinberg O, Vaadia E, Sompolinsky H.  2003.  Cortical representation of bimanual movements. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 23:11577–11586.
Rokni U, Sompolinsky H.  2011.  How the Brain Generates Movement. Neural computation. 24(2):289-331
Rubin N, Sompolinsky H.  1989.  Neural Networks with Low Local Firing Rates. Europhysics Letters 10, 465..
Rubin R, Monasson R, Sompolinsky H.  2010.  Theory of spike timing-based neural classifiers. Physical Review Letters. 105:218102.
Rubin J, Lee DD, Sompolinsky H.  2001.  Equilibrium properties of temporally asymmetric Hebbian plasticity. Physical Review Letters. 86:364–367.
Safran M, Flanagin V, Borst A, Sompolinsky H.  2007.  Adaptation and information transmission in fly motion detection. J Neurophysiol. 98:3309–3320.
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..
Seung HS, Sompolinsky H.  1993.  Simple models for reading neuronal population codes. Proceedings of the National Academy of Sciences of the United States of America. 90:10749–10753.
Seung HS, Sompolinsky H, Tishby N.  1992.  Statistical mechanics of learning from examples. Physical Review. A. 45:6056–6091.
Shamir M, Sompolinsky H.  2006.  Implications of neuronal diversity on population coding. Neural Computation. 18:1951–1986.

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