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- Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
- An Information Maximization Approach to Overcomplete and Recurrent Representations
- Statistical Mechanics of Compressed Sensing: Supplementary Material February 23, 2010
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- Adaptation without parameter change: Dynamic gain control in motion detection
- LETTER Communicated by Peter Latham Nonlinear Population Codes
- Behavioral/Systems/Cognitive Cortical Representation of Bimanual Movements
- Thouless-Anderson-Palmer equations for neural networks Maoz Shamir and Haim Sompolinsky
- Statistical Mechanics of Support Vector Networks Rainer Dietrich1
- On-line Gibbs learning. II. Application to perceptron and multilayer networks J. W. Kim and H. Sompolinsky
- 13 Modeling Feature Selectivity in Local Cortical Circuits David Hansel and Haim Sompolinsky
- Journal of Computational Neuroscience, 4, 5777 (1997) c 1997 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
- Proceedings of the Third NEC Research Symposium: HComputational Learning and CognHion" (SL!\rI191993)
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- Statistical Mechanics of Support Vector Networks Rainer Dietrich 1 , Manfred Opper 2 and Haim Sompolinsky 3
- Online Gibbs learning. I. General theory H. Sompolinsky and J. W. Kim
- VOLUME 86, NUMBER 21 P H Y S I C A L R E V I E W L E T T E R S 21 MAY 2001 Mutual Information of Population Codes and Distance Measures in Probability Space
- Learning From Examples in a Single Layer Neural Network D. Hanset+ and H. So'mpolinsky++
- Modeling Feature Selectivity in Local Cortical Circuits David Hansel
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- Adaptation and Information Transmission in Fly Motion Detection Moshe N. Safran,1,2
- Postdoctoral Positions in Theoretical Neuroscience at the Hebrew University We seek candidates for postdoctoral research in theoretical neuroscience at the
- Mutual Information of Population Codes and Distance Measures in Probability Space K. Kang and H. Sompolinsky
- Online Gibbs learning. II. Application to perceptron and multilayer networks J. W. Kim and H. Sompolinsky
- New perspectives on the mechanisms for orientation Haim Sompolinsky* and Robert Shapleyt
- ThoulessAndersonPalmer equations for neural networks Maoz Shamir and Haim Sompolinsky
- Chaotic Balanced State in a Model of Cortical C. van Vreeswijk and H. Sompolinsky
- Journal of Computational Neuroscience 3, 7-34 (1996)' @ 1996 Kluwer Academic Publishers. Manufactured in The Netherlands.
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- Comparison to the Hopfield-Brody model of time-warp invariant neuronal processing.
- STATISTICAL MECHANICS OF NEURAL NETWORKS
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- Correlation Codes in Neuronal Populations Maoz Shamir and Haim Sompolinsky
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