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Haim SompolinskyProfessor of PhysicsThe Racah Institute of Physics
§ Heimal J.A and Sompolinsky H Stable Orientation Tuning in the Visual Cortex. Proceedings of CNS 2000. (2000) (ps file) (pdf file) (zip file) § Shamir M and Sompolinsky H Thouless-Anderson-Palmer Equations for Neural Networks. Phys Rev E 61: 1839-1844.(2000) § Lee D.D, Rokni U and Sompolinsky H Algorithms for Independent Components Analysis and Higher Order Statistics. In Advances in Neural Information Processing Systems, 12. Solla S.A, Leen T.K and Müller K.R, Eds.(2000) (pdf file) § Dietrich R, Opper M and Sompolinsky H Statistical Mechanics of Support Vector Networks. Physical Review Letters 82: 2975. (1999) (ps file) (pdf file) (zip file) §
Yoon H and Sompolinsky H The Effect of Correlations on the
Fisher Information of Population Codes. In Advances in Neural Information
Processing Systems, 11. (pdf file) §
Lee D.D and Sompolinsky H Learning a Continuous Hidden Variable Model for Binary Data. In
Advances in Neural Information Processing Systems, 11. (pdf file) §
Sompolinsky H and Kim J.W
On-Line § Sompolinsky H and Kim J.W On-Line Gibbs Learning II. Application to Perceptron and Multilayer Networks. Physical Review E 58: 2348-2362. (1998) § Van Vreeswijk C and Sompolinsky H Chaotic Balanced State in a Model of Cortical Circuits. Neural Computation 10: 1321-1371. (1998) § Hansel D and Sompolinsky H Modeling Feature Selectivity in Local Cortical Circuits. In Methods in Neuronal Modeling: From Synapse to Networks. Koch C and Segev I, Eds, (MIT Press, Cambridge, MA, 1998), Chapter 13, second edition. (1998) (ps file) (pdf file) (figures) § Sompolinsky H and Shapley R New Perspectives on Mechanisms for Orientation Selectivity. Current Opinion in Neurobiol. 7: 514-522. (1997)
(pdf file) § Ben-Yishai R, Hansel D and Sompolinsky H Traveling Waves and the Processing of Weakly Tuned Inputs in a Cortical Network Model. Journal of Computational Neuroscience 4: 57-79. (1997)
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§ Hansel D and Sompolinsky H Chaos and Synchrony in a Model of a Hypercolumn in Visual Cortex. Journal of Computational Neuroscience 3:7-34. (1996)
(pdf file) § Van Vreeswijk C and Sompolinsky H Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity. Science 274: 1724-1726. (1996) § Mato G and Sompolinsky H Neural Network Models of Perceptual Learning of Angle Discrimination. Neural Computation 8: 270-29 (1996) (pdf file) § Kim J.W and Sompolinsky H (1996) On-line Gibbs Learning. Physical Review Letters 76: 3021-3024. (pdf file) § Barkai N, Seung HS and Sompolinsky H (1995) Local and Global Convergence of On-line Learning. Physical Review Letters 75: 1415-1418.
(pdf file) § Ben-Yishai R, Lev Bar-Or R and Sompolinsky H (1995) Theory of Orientation Tuning in Visual Cortex. Proc. Natl. Acad. Sci. USA 92: 3844-3848.
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§ Sompolinsky H, Barkai N and Seung HS (1995) On-line Learning of Dichotomies: Algorithms and Learning Curves. In Advances in Neural Information Processing Systems 7. Cowan J.D, Tesauro G, and Alspector J, Eds.
1990- 1994
(pdf file) § Sompolinsky H and Tsodyks M (1994) Segmentation by a Network of Oscillators with Stored Memories. Neural Computation 6: 642-657.
(pdf file) § Ginzburg I and Sompolinsky H (1994) Theory of Correlations in Stochastic Neural Networks. Physical Review E 50: 3171-3191.
(pdf file) § Ginzburg I and Sompolinsky H (1994) Correlation Functions in a Large Stochastic Neural Network. In Advances in Neural Information Processing Systems 6. Cowan J.D, Tesauro G, and Alspector J, Eds., 6: 471-476. (pdf file) § Barkai N and Sompolinsky H (1994) Statistical Mechanics of Maximul-Liklihood Destiny Estimation. Physical Review E, Vol. 50: 1766-1769. (pdf file) §
Seung H.S and Sompolinsky H (1993) Simple
Models for Reading Neuronal Population Codes. Proceedings of
(pdf file) § Hansel D and Sompolinsky H (1993) Solvable Model of Spatiotemporal Chaos. Physical Review Letters 71: 2710-2713. (pdf file) § Tsodyks M, Mitkov I and Sompolinsky H (1993) Pattern of Synchrony in Inhomogeneous Networks of Oscillators with Pulse Interactions. Physical Review Letters 71: 1280-3. (pdf file) §
Sompolinsky H and Barkai N (1993)
Theory of Learning from Examples. Tutorial Lecture, Int.
Joint Conf. on Neural Networks, (pdf file) § Tsodyks M, Mitkov I and Sompolinsky H (1993) Pattern of Synchrony in Integrate-and-Fire-Networks. In Proceedings of International Conference on Artificial Neural Networks, p. 622-627. (pdf file) § Barkai N, Seung H.S and Sompolinsky H (1993) Scaling Laws in Learning Classification Tasks. Physical Review Letters 70: 3167-70. (pdf file) § Sompolinsky H (1993) Theoretical Issues in Learning from Examples. In Proceedings of the Third NEC Symposium on Computational Learning and Cognition. (pdf file) § Grannan E.R, Sompolinsky H and Kleinfeld D (1993) Stimulus Dependent Synchronization of Neuronal Assemblies. Neural Computation, Vol. 5(4): 550. (pdf file) § Seung H.S , Opper M.A and Sompolinsky H (1992) Query by Committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Warmuth M.K and Valiant L.G, Eds. (Kaufmann, San Mateo, CA, 1992), p. 287. (pdf file) § Sompolinsky H and Tsodyks M (1992) Processing of Sensory Information by a Network of Oscillators with Memory. International Journal of Neural Systems Vol. 3: 51. (pdf file) § Aranson I, Golomb D and Sompolinsky H (1992) Spatial Coherence and Temporal Chaos in Macroscopic Systems. Physical Review Letters, Vol. 68: 3495. (pdf file) § Barkai E, Hansel D. and Sompolinsky H (1992) Broken Symmetries in Multilayered Perceptrons. Physical Review A, Vol. 45: 4146-4161. (pdf file) § Seung H.S, Sompolinsky H and Tishby N (1992) Statistical Mechanics of Learning from Examples. Physical Review A, Vol. 45: 6056-6091. (pdf file) § Hansel D and Sompolinsky H (1992) Synchronization and Computation in a Chaotic Neural Network. Physical Review Letters, Vol. 68: 718-721. (pdf file) § Golomb D, Hansel D, Shraiman B and Sompolinsky H (1992) Clustering in Globally Coupled Oscillators. Physical Review A, Vol. 45: 3516-3530. (pdf file) § Sompolinsky H, Golomb D and Kleinfeld D (1991) Cooperative Dynamics in Visual Processing. Physical Review A, Vol. 43: 6990-7011. (pdf file) § Kleinfeld D, Chiel H.J and Sompolinsky H (1991) Small Nervous Systems and Neural Network Models. in Non-Linear Dynamics and Neural Networks, Schuster H.G and Singer W, Eds. (VCH, Weinheim, 1991), pp. 77-109. (pdf file) § Sompolinsky H, Golomb D and Kleinfeld D (1991) Phase Coherence and Computation in a Neural Network of Coupled Oscillators. in Non-Linear Dynamics and Neural Networks, Schuster H.G and Singer W, Eds. (VCH, Weinheim, 1991), pp. 113-140. (pdf file) § Sompolinsky H, Seung S and Tishby N (1990) Learning in a Two-Layer Neural Network of Edge Detectors. Europhysics Letters. Vol. 13: 567. (pdf file) § Sompolinsky H, Tishby N and Seung S (1990) Learning from Examples in Large Neural Networks. Physical Review Letters, Vol. 65, 1683-6. (pdf file) §
Sompolinsky H,
Golomb D and Kleinfeld D
(1990) Global Processing of Visual Stimuli in a Neural Network of Coupled
Oscillators. Proceedings of (pdf file) § Hansel D and Sompolinsky H (1990) Learning from Examples in a Single-Layer Neural Network. Europhysics Letters, Vol. 11: 687. (pdf file) § Golomb D, Rubin N and Sompolinsky H (1990) Willshaw Model: Associative Memory with Sparse Coding and Low Firing Rates. Physical Review A, Vol. 41: 1843-1854. (pdf file) § Barkai E, Kanter I and Sompolinsky H (1990) Properties of Sparsely Connected Excitatory Neural Networks. Physics Review A, Vol. 41: 590-7. (pdf file) 1985- 1989
§ Rubin N and Sompolinsky H (1989) Neural Networks with Low Local Firing Rates. Europhysics Letters, Vol. 10: 465. (pdf file) §
Kleinfeld D and Sompolinsky H (1989) Associative
Network Models for Central Pattern Generators. In Methods in Neuronal
Modeling: From Synapse to Networks, Koch C and Segev
I, Eds., (pdf file) § Sompolinsky H (1988) Statistical Mechanics of Neural Networks. Physics Today, Vol. 40: 70. (pdf file) § Kleinfeld D and Sompolinsky H (1988) Associative neural Network Models for the Generation of Temporal Patterns: Theory and Application to Central Pattern Generators. Biophysical Journal, Vol. 54: 1039-1051. § Sompolinsky H, Crisanti A and Sommers H.J (1988) Chaos in Random Neural Networks. Physical Review Letters, Vol. 61: 259-262. (pdf file) § Sommer H.J, Crisanti A, Sompolinsky H and Stein Y (1988) The Spectrum of Large Random Asymmetric Matrices. Physical Review Letters, Vol. 60: 1895-1899. (pdf file) § Crisanti A and Sompolinsky H (1988) Dynamics of Spin Systems with Randomly Asymmetric Bonds: Ising Spins and Glauber Dynamics. Physical Review A, Vol. 37: 4865-4874. (pdf file) §
(pdf file) § Crisanti A and Sompolinsky H (1987) Dynamics of Spin Systems with Randomly Asymmetric Bonds: Langevin Dynamics and a Spherical Model. Physical Review A, Vol. 36: 4922-4939. (pdf file) §
Sompolinsky
H (1987) The Theory of Neural Networks:
The Hebb Rule and Beyond. Proceedings of
(pdf file) § Amit D.J, Gutfreund H and Sompolinsky H (1987) Information Storage in Neural Networks with Low Levels of Activity. Physical Review A, Vol. 35: 2293-2303. (pdf file) § Kanter I and Sompolinsky H (1987) Associative Recall of Memory Without Errors. Physical Review A, Vol. 35: 380-392. (pdf file) § Amit D.J, Gutfreund H and Sompolinsky H (1987) Statistical Mechanics of Neural Networks Near Saturation. Ann. Phys. (NY) 173, 30 . (pdf file) § Sompolinsky H (1986) Neural Networks with Non-Linear Synapses and a Static Noise. Physical Review A, Vol. 34: 2571. (pdf file) § Yeshurun Y and Sompolinsky H (1986) Effect of Gold Impurities on the Critical Properties CuMn Spin-Glasses. Physical Review Letters, Vol. 56: 984. (pdf file) § Kanter I and Sompolinsky H (1986) Temporal Association in Asymmetric Neural Networks. Physical Review Letters, Vol. 57: 2861-2864. (pdf file) § Amit D.J, Gutfreund H and Sompolinsky H (1985) Storing Infinite Number of Patterns in a Spin Glass Model for Neural Networks. Physical Review Letters, Vol. 55, 1530-3. (pdf file) § Amit D.J, Gutfreund H and Sompolinsky H (1985) Spin Glass Models of Neural Networks. Physical Review A, Vol. 32: 1007-1018. (pdf file)
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