Haim Sompolinsky Lab

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William N. Skirball Chair in Neurophysics, Emeritus

Sompolinsky’s research goal is to uncover the fundamental principles of the organization, the dynamics and the function of the brain, viewing the brain through multiscale lenses, spanning the molecular, the cellular, and the circuit levels. To achieve this goal, Sompolinsky has developed new theoretical approaches to computational neuroscience based on the principles and methods of statistical physics, and physics of dynamical and stochastic systems. This new field, Neurophysics, builds in part on Sompolinsky’s earlier work on critical phenomena, random systems, spin glasses, and chaos. His research areas cover theoretical and computational investigations of cortical dynamics, sensory processing, motor control, neuronal population coding, long and short-term memory, and neural learning. The highlights of his research include theories and models of local cortical circuits, visual cortex, associative memory, statistical mechanics of learning, chaos and excitation-inhibition balance in neuronal networks, principles of neural population codes, statistical mechanics of compressed sensing and sparse coding in neuronal systems, and the Tempotron model of spike time based neural learning. He also studies the neuronal mechanisms of volition and the impact of physics and neuroscience on the foundations of human freedom and agency.

Ariel Furstenberg

Research Associate

Nimrod Shaham

Post Doc

Uri Cohen

PhD Student

Gadi Mintz

PhD Student

Qianyi Li, Haim Sompolinsky

Statistical Mechanics of Deep Linear Neural Networks: The Backpropagating Kernel Renormalization

Phys. Rev. X, Volume 11, Issue 3, Pages 031059 (2021)

Gadi Naveh, Oded Ben David, Haim Sompolinsky, and Zohar Ringel

Predicting the outputs of finite deep neural networks trained with noisy gradients

Phys. Rev. E 104, 064301 (2021)

Shani Narkiss H., Vingrad A., Landau I.D., Tasaka G., Yayun N., Terlestky S., Groysman M., Maor I., Sompolinsky H., and Mizrahi. A

Adult Born Neurons Improve Odor Coding by Mitral Cells

Nature Communications (2020)

Cohen, U., Chung, S., Lee, D.D. et al.

Separability and geometry of object manifolds in deep neural networks

Nat Commun 11, 746 (2020)

Maor Ido, Shwartz-Ziv Ravid, Feigin Libi, Elyada Yishai, Sompolinsky Haim, Mizrahi Adi

Neural correlates of learning pure tones versus natural sounds in the auditory cortex

Frontiers in Neural Circuits VOLUME=13,PAGES=82 (2020)

Gjorgjieva J, Meister M, Sompolinsky H

Functional diversity among sensory neurons from efficient coding principles

PLOS Computational Biology (2019)

Furstenberg A, Dewar CD, Sompolinsky H, Knight RT, Deouell LY

Effect of Aging on Change of Intention

Frontiers in Human Neuroscience 13:264 (2019)

A Crisanti, H Sompolinsky

Path integral approach to random neural networks

Phys. Rev. E 98, 062120 – Published 13 December 2018 (2018)

Chung S, Cohen U, Sompolinsky H, Lee DD.

Learning Data Manifolds with a Cutting Plane Method.

Neural Computation Volume 30 | Issue 10 | October 2018 p.2593-2615 (2018)

Itamar Daniel Landau, Haim Sompolinsky

Coherent chaos in a recurrent neural network with structured connectivity

PLOS Computational Biology (2018)

Chen X, Mu Y, Hu Y, Kuan AT, Nikitchenko M, Randlett O, Chen AB, Gavornik JP, Sompolinsky H, Engert F, Ahrens MB.

Brain-wide Organization of Neuronal Activity and Convergent Sensorimotor Transformations in Larval Zebrafish

Neuron: Volume 100, Issue 4, 21 November 2018, Pages 876-890.e5 (2018)

Litwin-Kumar A, Harris KD, Axel R, Sompolinsky H, Abbott LF.

Optimal Degrees of Synaptic Connectivity

Neuron: Volume 93, Issue 5, 8 March 2017, Pages 1153-1164.e7 (2017)

Rubin R, Abbott LF, Sompolinsky H.

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

PNAS October 31, 2017 114 (44) E9366-E9375; (2017)

Landau I, Egger R, Dercksen V, Oberlaender M, Sompolinsky H.

The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks

Neuron Volume 92, Issue 5, 7 December 2016, Pages 1106-1121 (2016)

Furstenberg, A., Breska, A., Sompolinsky, H., & Deouell, L. Y.

Evidence of Change of Intention in Picking Situations

Journal of Cognitive Neuroscience. 27, 2133-46 (2015)

Furstenberg A, Breska A, Sompolinsky H, Deouell LY.

Change of Intention in ‘Picking’ Situations

J Cogn Neurosci. 2015 Nov;27(11):2133-46. doi: 10.1162/jocn_a_00842. Epub 2015 Jun 23. (2015)

Baktash Babadi; Haim Sompolinsky

Sparseness and Expansion in Sensory Representations

Neuron: Volume 83, Issue 5, 3 September 2014, Pages 1213-1226 (2014)

Cengiz Pehlevan; Haim Sompolinsky

Selectivity and Sparseness in Randomly Connected Balanced Networks

PLOS ONE, Volume 9, Issue 2 (2014) (2014)

Raoul-Martin Memmesheimer; Ran Rubin; Bence P. Olveczky; Haim Sompolinsky

Learning Precisely Timed Spikes

Neuron, Volume 84, Issue 4 (2014) (2014)

Haim Sompolinsky

Computational neuroscience: beyond the local circuit

Current Opinion in Neurobiology Volume 25, April 2014, Pages xiii-xviii (2014)

Julijana Gjorgjieva; Haim Sompolinsky; Markus Meister

Benefits of pathway splitting in sensory coding

Journal of Neuroscience 3 September 2014, 34 (36) 12127-12144; DOI: https://doi.org/10.1523/JNEUROSCI.1032-14.2014 (2014)

M. Stern, H. Sompolinsky, and L. F. Abbott

Dynamics of Random Neural Networks with Bistable Units

Phys. Rev. E 90, 062710 – Published 16 December 2014 (2014)

Surya Ganguli; Haim Sompolinsky

Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis

Annual Review of Neuroscience, Volume 35:485–508 (2012) (2012)

Yoram Burak, Uri Rokni, Markus Meister, and Haim Sompolinsky

Reply to Wehrhahn: Experimental requirements for testing the role of peripheral cues in dynamic image stabilization

PNAS 108 (10) E36 (2011)

Uri Rokni; Haim Sompolinsky

How the Brain Generates Movement

Neural computation, Volume 24(2):289-331 (2011) (2011)

Rubin R, Monasson R, Sompolinsky H.

Theory of spike timing-based neural classifiers

Phys Rev Lett. 105(21):218102 (2010)

Rajan K, Abbott LF, Sompolinsky H.

Stimulus-dependent suppression of chaos in recurrent neural networks

Phys Rev E Stat Nonlin Soft Matter Phys. 82(1 Pt 1):011903. (2010)

Surya Ganguli; Haim Sompolinsky

Statistical mechanics of compressed sensing

Physical Review Letters, Volume 104, p.188701 (2010)

Ganguli. S, H. Sompolinsky

Short-term memory in neuronal networks through dynamical compressed sensing

Neural Information Processing Systems (2010) (2010)

Yoram Burak; Uri Rokni; Markus Meister; Haim Sompolinsky

Bayesian model of dynamic image stabilization in the visual system

Proceedings of the National Academy of Sciences (USA), doi/10.1073/pnas.1006076107 (2010) (2010)

Rajan, K; L F Abbott; H Sompolinsky

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics

Neural Information Processing Systems (2010) (2010)

Gütig R, Sompolinsky H.

Time-warp-invariant neuronal processing

PLoS Biol. 7(7):e1000141 (2009)

Yoram Burak, Sam Lewallen, Haim Sompolinsky

Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons

Neural Computation Volume 21 | Issue 8 | p.2269-2308 (2009)

Ganguli S, Huh D, Sompolinsky H.

Memory traces in dynamical systems

Proceedings of the National Academy of Sciences(USA), Volume 105, p.18970–18975 (2008)

Ilana B Witten; Eric I Knudsen; Haim Sompolinsky

A Hebbian learning rule mediates asymmetric plasticity in aligning sensory representations

J Neurophysiol. 100(2):1067-79. (2008)

Safran MN, Flanagin VL, Borst A, Sompolinsky H.

Adaptation and information transmission in fly motion detection

J Neurophysiol. 98(6):3309-20. (2007)

Pitkow X, Sompolinsky H, Meister M.

A neural computation for visual acuity in the presence of eye movements

PLoS Biol. 5(12):e331. (2007)

Yonatan Loewenstein, Séverine Mahon, Paul Chadderton, Kazuo Kitamura, Haim Sompolinsky, Yosef Yarom & Michael Häusser

Purkinje cells in awake behaving animals operate in stable upstate membrane potential

Nature Neurosciencevolume 9, page461 (2006)

Robert Gütig; Haim Sompolinsky

The tempotron: a neuron that learns spike timing-based decisions

Nature Neuroscience, Volume 9, p.420–428 (2006)

Shamir M, Sompolinsky H.

Implications of neuronal diversity on population coding

Neural Computation, Volume 18, p.1951–1986 (2006)

Yonatan Loewenstein; Séverine Mahon; Paul Chadderton; Kazuo Kitamura; Haim Sompolinsky; Yosef Yarom; Michael Häusser

Bistability of cerebellar Purkinje cells modulated by sensory stimulation

Nature Neuroscience, Volume 8, p.202–211 (2005)

Alexander Borst; Virginia L Flanagin; Haim Sompolinsky

Adaptation without parameter change: Dynamic gain control in motion detection

Proc Natl Acad Sci U S A. ;102(17):6172-6. (2005)

Olivia L White; Daniel D Lee; Haim Sompolinsky

Short-term memory in orthogonal neural networks

Physical Review Letters, Volume 92, p.148102 (2004) (2004)

Joshua A Goldberg; Uri Rokni; Haim Sompolinsky

Patterns of ongoing activity and the functional architecture of the primary visual cortex

Neuron, Volume 42, p.489–500 (2004) (2004)

Maoz Shamir; Haim Sompolinsky

Nonlinear population codes

Neural Computation, Volume 16, p.1105–1136 (2004)

Kang K, Shapley RM, Sompolinsky H.

Information tuning of populations of neurons in primary visual cortex

J Neurosci. 24(15):3726-35 (2004)

Yonatan Loewenstein; Haim Sompolinsky

Temporal integration by calcium dynamics in a model neuron

Nat Neurosci. 2003 Sep;6(9):961-7. (2003)

Oren Shriki; David Hansel; Haim Sompolinsky

Rate models for conductance-based cortical neuronal networks

Neural Comput. 2003 Aug;15(8):1809-41. (2003)

Vladimir Litvak; Haim Sompolinsky; Idan Segev; Moshe Abeles

On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance

J Neurosci. 2003 Apr 1;23(7):3006-15. (2003)

Kukjin Kang; Michael Shelley; Haim Sompolinsky

Mexican hats and pinwheels in visual cortex

Proc Natl Acad Sci U S A. 2003 Mar 4;100(5):2848-53. (2003)

Robert Gütig; R Aharonov; Rotter, S.; Haim Sompolinsky

Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity

J Neurosci. 2003 May 1;23(9):3697-714. (2003)

Uri Rokni; Orna Steinberg; Eilon Vaadia; Haim Sompolinsky

Cortical representation of bimanual movements

J Neurosci. 2003 Dec 17;23(37):11577-86. (2003)

Yonatan Loewenstein; Haim Sompolinsky

Oscillations by symmetry breaking in homogeneous networks with electrical coupling

Phys Rev E Stat Nonlin Soft Matter Phys. 2002 May;65(5 Pt 1):051926 (2002)

Loewenstein Y, Yarom Y, Sompolinsky H.

The generation of oscillations in networks of electrically coupled cells

Proc Natl Acad Sci U S A. 2001 Jul 3;98(14):8095-100 (2001)

Sompolinsky H, Yoon H, Kang K, Shamir M.

Population coding in neuronal systems with correlated noise

Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Nov;64(5 Pt 1):051904 (2001)

Kang K, Sompolinsky H.

Mutual information of population codes and distance measures in probability space

Phys Rev Lett. 2001 May 21;86(21):4958-61 (2001)

Rubin J, Lee DD, Sompolinsky H.

Equilibrium properties of temporally asymmetric Hebbian plasticity

Phys Rev Lett. 2001 Jan 8;86(2):364-7 (2001)

Maoz Shamir and Haim Sompolinsky

Correlation codes in neuronal networks

In Advances in Neural Information Processing Systems 14. Dietterich T., Becker S., and Ghahramani Z., Editors. MIT Press, Cambridge MA (2001)

Shriki O., Sompolinsky H., Lee D.

An Information Maximization approach to overcomplete and recurrent representations

Part of: Advances in Neural Information Processing Systems 13 (NIPS 2000) (2001)

Alexander Heimal; Haim Sompolinsky

Stable Orientation Tuning in the Visual Cortex

Preprint submitted to Elsevier Preprint (2000) (2000)

Shamir M, Sompolinsky H.

Thouless-anderson-palmer equations for neural networks

Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000 Feb;61(2):1839-44 (2000)

Daniel D Lee; Haim Sompolinsky

Learning a continuous hidden variable model for binary data

Advances in Neural Information Processing Systems 11. Kearns M.J., Solla S.A., and Cohn D.A., Editors. MIT Press, Cambridge MA. (1999)

Hyoungsoo Yoon; Haim Sompolinsky

The Effect of Correlations on the Fisher Information of Population Codes

Advances in neural information processing systems · June 1999 (1999)

Rainer Dietrich, Manfred Opper, and Haim Sompolinsky

Statistical Mechanics of Support Vector Networks

Phys. Rev. Lett. 82, 2975 – Published 5 April 1999 (1999)

Lee D., Rokni U., Sompolinsky H.

Algorithms for independent components analysis and higher order statistics

Advances in Neural Information Processing Systems 12. Kearns M.J., Solla S.A., and Cohn D.A., Editors. MIT Press, Cambridge MA. (2000) (1999)

David Hansel; Haim Sompolinsky

Modeling Feature Selectivity in Local Cortical Circuits. In Methods in Neuronal Modeling: From Synapse to Networks

MIT Press, Cambridge, MA, Chapter 13, second edition. (1998) (1998)

J. W. Kim and H. Sompolinsky

On-line Gibbs Learning. II. Application to Perceptron and multilayer networks

Phys. Rev. E 58, 2348 (1998)

H Sompolinsky; Kim J.

On-line Gibbs learning. I. General Theory

Physical Review E, 58: 2335-2347. (1998) (1998)

Van Vreeswijk C, Sompolinsky H.

Chaotic balanced state in a model of cortical circuits

Neural Comput. 1998 Aug 15;10(6):1321-71 (1998)

Ben-Yishai R, Hansel D, Sompolinsky H.

Traveling waves and the processing of weakly tuned inputs in a cortical network module

J Comput Neurosci. 1997 Jan;4(1):57-77. (1997)

Sompolinsky H, Shapley R.

New perspectives on the mechanisms for orientation selectivity

Curr Opin Neurobiol. 1997 Aug;7(4):514-22. (1997)

Kim JW, Sompolinsky H.

On-line Gibbs learning

Phys Rev Lett. 1996 Apr 15;76(16):3021-3024. (1996)

Mato G, Sompolinsky H.

Neural network models of perceptual learning of angle discrimination

Neural Comput. 1996 Feb 15;8(2):270-99. (1996)

van Vreeswijk C, Sompolinsky H.

Chaos in neuronal networks with balanced excitatory and inhibitory activity

Science. 1996 Dec 6;274(5293):1724-6. (1996)

Hansel D, Sompolinsky H.

Chaos and synchrony in a model of a hypercolumn in visual cortex

J Comput Neurosci. 1996 Mar;3(1):7-34. (1996)

H. Sompolinsky; Barkai, N.; H S Seung

On-line Learning of Dichotomies: Algorithms and Learning Curves

Advances in Neural Information Processing Systems. Cowan J.D., Tesauro G. and Alspector J., editors, 7. (1995)

Barkai N, Seung HS, Sompolinsky H.

Local and global convergence of on-line learning

Phys Rev Lett. 1995 Aug 14;75(7):1415-1418. (1995)

Ben-Yishai R, Bar-Or RL, Sompolinsky H.

heory of orientation tuning in visual cortex

Proc Natl Acad Sci U S A. 1995 Apr 25;92(9):3844-8. (1995)

Ginzburg I I, Sompolinsky H.

Theory of correlations in stochastic neural networks

Phys. Rev. E 50, 3171 – Published 1 October 1994 (1994)

Barkai N, Sompolinsky H.

Statistical mechanics of the maximum-likelihood density estimation

Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1994 Sep;50(3):1766-1769. (1994)

H. Sompolinsky and M. Tsodyks

Segmentation by a Network of Oscillators with Stored Memories

Neural Computation Volume 6 | Issue 4 | July 1994 p.642-657 (1994)

Haim Sompolinsky

Theoretical Issues in Learning from Examples

Proceedings of the Third NEC Symposium on Computational Learning and Cognition. (1993) (1993)

E. R. Grannan, D. Kleinfeld and H. Sompolinsky

Stimulus-Dependent Synchronization of Neuronal Assemblies

Neural Computation Volume 5 | Issue 4 | July 1993 p.550-569 (1993)

D. Hansel and H. Sompolinsky

Solvable model of spatiotemporal chaos

Phys. Rev. Lett. 71, 2710 – Published 25 October 1993 (1993)

Seung HS, Sompolinsky H.

Simple models for reading neuronal population codes

Proc Natl Acad Sci U S A. 1993 Nov 15;90(22):10749-53. (1993)

N. Barkai, H. S. Seung, and H. Sompolinsky

Scaling laws in learning of classification tasks

Phys. Rev. Lett. 70, 3167 – Published 17 May 1993 (1993)

Tsodyks Misha; I Mitkov; Haim Sompolinsky

Pattern of Synchrony in Integrate-and-Fire-Networks

Proceedings of International Conference on Artificial Neural Networks, p. 622-627. (1993) (1993)

Tsodyks M, Mitkov I I, Sompolinsky H.

Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions

Phys. Rev. Lett. 71, 1280 – Published 23 August 1993 (1993)

Ginzburg I., Sompolinsky H.

Correlation Functions in a Large Stochastic Neural Network

Advances in Neural Information Processing Systems. Cowan J.D., Tesauro G. and Alspector J., editors, 6, 471-476 (1993)

D. Hansel and H. Sompolinsky

Synchronization and computation in a chaotic neural network

Phys. Rev. Lett. 68, 718 – Published 3 February 1992 (1992)

Seung HS, Sompolinsky H, Tishby N.

Statistical mechanics of learning from examples

Phys. Rev. A 45, 6056 – Published 1 April 1992 (1992)

Aranson I I, Golomb D, Sompolinsky H.

Spatial coherence and temporal chaos in macroscopic systems with asymmetrical couplings

Phys Rev Lett. 1992 Jun 15;68(24):3495-3498. (1992)

Seung HS, Opper M.A., Sompolinsky H.

Query by Committee

Proceeding COLT '92 Proceedings of the fifth annual workshop on Computational learning theory Pages 287-294 (1992)

Tsodyks M., Sompolinsky H.

Processing of Sensory Information by a Network of Oscillators with Memory

International Journal of Neural SystemsVol. 03, No. supp01, pp. 51-56 (1992) (1992)

Golomb D, Hansel D, Shraiman B, Sompolinsky H.

Clustering in globally coupled phase oscillators

Phys Rev A. 1992 Mar 15;45(6):3516-3530. (1992)

Barkai E, Hansel D, Sompolinsky H.

Broken symmetries in multilayered perceptrons

Phys Rev A. 1992 Mar 15;45(6):4146-4161. (1992)

David Kleinfeld; Hillel J Chiel; Haim Sompolinsky

Small Nervous Systems and Neural Network Models

Non-Linear Dynamics and Neural Networks, Schuster H.G and Singer W, Eds. (VCH, Weinheim, 1991), pp. 77-109. (1991) (1991)

Haim Sompolinsky; Golomb D. Kleinfeld D.

Phase Coherence and Computation in a Neural Network of Coupled Oscillators

Non-Linear Dynamics and Neural Networks, Schuster H.G and Singer W, Eds. (VCH, Weinheim, 1991), pp. 113-140. (1991) (1991)

Sompolinsky H, Golomb D, Kleinfeld D.

Cooperative dynamics in visual processing

Phys Rev A. 1991 Jun 15;43(12):6990-7011. (1991)

D. Golomb, N. Rubin, and H. Sompolinsky

Willshaw model: Associative memory with sparse coding and low firing rates

Phys. Rev. A 41, 1843 – Published 1 February 1990 (1990)

E. Barkai, I. Kanter, and H. Sompolinsky

Properties of sparsely connected excitatory neural networks

Phys. Rev. A 41, 590 – Published 1 January 1990 (1990)

H. Sompolinsky and N. Tishby

Learning in a Two-Layer Neural Network of Edge Detectors

Europhysics Letters 13, 567 (1990)

H. Sompolinsky, N. Tishby, and H. S. Seung

Learning from examples in large neural networks

Phys. Rev. Lett. 65, 1683 – Published 24 September 1990 (1990)

D. Hansel and H. Sompolinsky

Learning from Examples in a Single-Layer Neural Network

Europhysics Letters 11, 687 (1990)

Sompolinsky H, Golomb D, Kleinfeld D.

Global processing of visual stimuli in a neural network of coupled oscillators

Proc Natl Acad Sci U S A. 1990 Sep;87(18):7200-4. (1990)

E.R. Yacoby, Y. Wolfus, Y. Yeshurun, I. Felner, H. Sompolinsky

Scaling of the irreversible magnetization curves of YBaCuO

Physica C: Superconductivity and its Applications Volumes 162–164, Part 1, December 1989, Pages 697-698 (1989)

N. Rubin and H. Sompolinsky

Neural Networks with Low Local Firing Rates

EPL (Europhysics Letters), Volume 10, Number 5 (1989)

Y. Wolfus, Y. Yeshurun, I. Felner, and H. Sompolinsky

Scaling properties of the irreversible magnetization curves of high-temperature superconductors

Phys. Rev. B 40, 2701(R) – Published 1 August 1989 (1989)

Kleinfeld D, Sompolinsky H.

Associative Network Models for Central Pattern Generators

Biophys J 1989 Mar;55(3):following 583. (1989)

H. J. Sommers, A. Crisanti, H. Sompolinsky, and Y. Stein

Spectrum of large random asymmetric matrices

Phys. Rev. Lett. 60, 1895 – Published 9 May 1988 (1988)

H. Sompolinsky, A. Crisanti, and H. J. Sommers

Chaos in random neural networks

Phys. Rev. Lett. 61, 259 – Published 18 July 1988 (1988)

Haim Sompolinsky

Statistical Mechanics of Neural Networks

Physics Today 40, 70. (1988) (1988)

Crisanti A, Sompolinsky H.

Dynamics of spin systems with randomly asymmetric bonds: Ising spins and Glauber dynamics

Phys Rev A Gen Phys. 1988 Jun 15;37(12):4865-4874. (1988)

Kleinfeld D, Sompolinsky H.

Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators

Biophys J. 1988 Dec;54(6):1039-51. (1988)

H. Sompolinsky

The Theory of Neural Networks: The Hebb Rule and Beyond

Heidelberg Colloquium on Glassy Dynamics pp 485-527 (1987)

Kanter I I, Sompolinsky H.

Mean-field theory of spin-glasses with finite coordination number

Phys Rev Lett. 1987 Jan 12;58(2):164-167. (1987)

Amit DJ, Gutfreund H, Sompolinsky H.

Information storage in neural networks with low levels of activity

Phys Rev A Gen Phys. 1987 Mar 1;35(5):2293-2303. (1987)

I Kanter and H Sompolinsky

Graph Optimization Problems and the Potts Glass

Journal of Physics A General Physics 20(11):L673 (1987)

Daniel J Amit, Hanoch Gutfreund, Haim Sompolinsky

Statistical Mechanics of Neural Networks Near Saturation

Annals of Physics 173(1):30-67 (1987)

A J Bray, H Sompolinsky and C Yu

On the “Naive” Mean-Field Equations for Spin Glasses

Journal of Physics C: Solid State Physics, Volume 19, Number 32 (1987)

Yeshurun Y, Felner I I, Sompolinsky H.

Magnetic properties of a high-Tc superconductor YBa2Cu3O7: Reentry-like features, paramagnetism, and glassy behavior

Phys. Rev. B 36, 840(R) – Published 1 July 1987 (1987)

Crisanti A, Sompolinsky H.

Dynamics of spin systems with randomly asymmetric bonds: Langevin dynamics and a spherical model

Phys Rev A Gen Phys. 1987 Nov 15;36(10):4922-4939. (1987)

Kanter I I, Sompolinsky H.

Associative recall of memory without errors

Phys Rev A Gen Phys. 1987 Jan 1;35(1):380-392. (1987)

Kotliar G, Sompolinsky H, Zippelius A.

Rotational symmetry breaking in Heisenberg spin glasses: A microscopic approach

Phys Rev B Condens Matter. 1987 Jan 1;35(1):311-328. (1987)

H. Sompolinsky and I. Kanter

Temporal association in asymmetric neural networks

Phys. Rev. Lett. 57, 2861 – Published 1 December 1986 (1986)

H. Sompolinsky

Neural networks with nonlinear synapses and a static noise

Phys. Rev. A 34, 2571(R) – Published 1 September 1986 (1986)

I. Kanter and H. Sompolinsky

Level repulsion and the dielectric relaxation in quadrupolar glasses

Phys. Rev. B 33, 2073(R) – Published 1 February 1986 (1986)

Y. Yeshurun and H. Sompolinsky

Effect of Random Anisotropy on the Critical Properties of CuMn Spin Glasses

Phys. Rev. Lett. 56, 984 – Published 3 March 1986 (1986)

Y. Yeshurun and H. Sompolinsky

Effect of gold impurities on the critical properties of {CuMn} spin-glasses

Phys. Rev. Lett. 56, 984 – Published 3 March 1986 (1986)

Yeshurun Y, Sompolinsky H.

Transverse ordering in anisotropic spin glasses

Phys Rev B Condens Matter. 1985 Mar 1;31(5):3191-3193. (1985)

Amit DJ, Gutfreund H, Sompolinsky H.

Storing infinite numbers of patterns in a spin-glass model of neural networks

Phys Rev Lett. 1985 Sep 30;55(14):1530-1533. (1985)

H. Sompolinsky

Spin-glass transition in a magnetic field

Philosophical Magazine B Volume 51, 1985 - Issue 5 (1985)

Daniel J. Amit, Hanoch Gutfreund, and H. Sompolinsky

Spin-glass models of neural networks

Phys. Rev. A 32, 1007 – Published 1 August 1985 (1985)

Fisher DS, Sompolinsky H.

Scaling in spin-glasses

Phys Rev Lett. 1985 Mar 11;54(10):1063-1066. (1985)

Gross DJ, Kanter I I, Sompolinsky H.

Mean-field theory of the Potts glass

Phys Rev Lett. 1985 Jul 15;55(3):304-307. (1985)

G Kotliar; Haim Sompolinsky

Phase Transition in a Szyaloshinksy-Moriya Spin Glass

Phys. Rev. Lett. 53, 1751 – Published 29 October 1984 (1984)

H. Sompolinsky, G. Kotliar, and A. Zippelius

Exchange Stiffness and Macroscopic Anisotropy in Heisenberg Spin-Glasses

Phys. Rev. Lett. 52, 392 – Published 30 January 1984 (1984)

H. Sompolinsky and Annette Zippelius

Fluctuations in Short-Range Spin-Glasses

Phys. Rev. Lett. 50, 1297 – Published 25 April 1983 (1983)

Havlin S, Ben-Avraham D, Sompolinsky H.

Scaling behavior of diffusion on percolation clusters

Physical Review A 27(3) · March 1983 (1983)

Sajeev John, H. Sompolinsky, and Michael J. Stephen

Localization in a disordered elastic medium near two dimensions

Phys. Rev. B 27, 5592 – Published 1 May 1983 (1983)

C. Dasgupta and H. Sompolinsky

Equivalence of statistical-mechanical and dynamic descriptions of the infinite-range Ising spin-glass

Phys. Rev. B 27, 4511(R) – Published 1 April 1983 (1983)

Christopher L. Henley, H. Sompolinsky, and B. I. Halperin

Spin-resonance frequencies in spin-glasses with random anisotropies

Phys. Rev. B 25, 5849 – Published 1 May 1982 (1982)

Haim Sompolinsky; A Zippelius

Relaxational dynamics of the infinite-ranged spin glass with n-component spins

Journal of Physics C 15, L 1059. (1982) (1982)

H. Sompolinsky and Annette Zippelius

Relaxational dynamics of the Edwards-Anderson model and the mean-field theory of spin-glasses

Phys. Rev. B 25, 6860 – Published 1 June 1982 (1982)

Y. Yeshurun and H. Sompolinsky

Irreversible Response in Spin Glasses: An Experimental Study in Amorphous Fe-Mn

Phys. Rev. B 26, 1487(R) – Published 1 August 1982 (1982)

S. Havlin and H. Sompolinsky

Cluster approximation for q -dependent correlations in magnetic and ferroelectric systems

Phys. Rev. B 25, 5828 – Published 1 May 1982 (1982)

S Havlin, R A Cowley and H Sompolinsky

Anisotropic Polarization Fluctuations in KD2PO4-Type Crystals

Journal of Physics C: Solid State Physics, Volume 15, Number 29 (1982)

H. Sompolinsky and Amnon Aharony

Universal ratios among corrections-to-scaling amplitudes in the large- n limit

Phys. Rev. B 23, 3523 – Published 1 April 1981 (1981)

H. Sompolinsky

Staggered-magnetization approach to spin-glasses

Phys. Rev. B 23, 1371 – Published 1 February 1981 (1981)

H. Sompolinsky

Time-Dependent Order Parameters in Spin-Glasses

Phys. Rev. Lett. 47, 935 – Published 28 September 1981 (1981)

H. Sompolinsky and Annette Zippelius

Dynamic Theory of the Spin Glass Phase

Phys. Rev. Lett. 47, 359 – Published 3 August 1981 (1981)

Havlin S, Cowley RA, Sompolinsky H.

The Wavevector Dependence of the Polarization Fluctuations in KD2PO4

Ferroelectrics Pages 355-358 (1980)

Havlin S, Sompolinsky H.

Wave vector dependent susceptibility of KD2PO4 in the four cluster approximation

Ferroelectrics Pages 1079-1082 (1980)

S Havlin and H Sompolinsky

Low-Frequency Dynamics of KH2PO4 Type Crystals

Journal of Physics C: Solid State Physics, Volume 12, Number 15 (1979)

Sompolinsky H.; Havlin S.

Effect of Short-Range Interactions on the Transverse Dynamical Properties of KD2PO4

Phys. Rev. B 16, 3223 – Published 1 October 1977 (1977)

S. Havlin, E. Litov, and H. Sompolinsky

Unified Model for the Transverse Electric Susceptibility in KH2PO4 and NH4H2PO4 Type Crystals

Phys. Rev. B 14, 1297 – Published 1 August 1976 (1976)

Havlin S., Litov E., Sompolinsky H.

Theoretical and Experimental Studies of the Transverse Dielectric Properties of KD2PO4

Physical Review B (Solid State), Volume 13, Issue 11, June 1, 1976, pp.4999-5006 (1976)

Shlomo Havlin and Haim Sompolinsky

Coupling Between Protonic E-Mode and Lattice Vibrations in KDP- Type Crystals

Ferroelectrics 14(1):617-618 · January 1976 (1976)

S. Havlin, H. Sompolinsky

Central E-Mode and Transverse Dynamical Properties of KDP-Type Crystals

Physics Letters A Volume 57, Issue 2, 31 May 1976, Pages 171-172 (1976)

S. Havlin, E. Litov, H. Sompolinsky

The Transverse Dielectric Properties of KH2PO4

Physics Letters A 51(1):33-35 · January 1975 (1975)

S. Havlin, E. Litov, H. Sompolinsky

The Anomalous Temperature Dependence of the Protonic E- Mode in KDP-Type Crystals

Physics Letters A Volume 53, Issue 1, 19 May 1975, Pages 41-42 (1975)

Honors:
Member, Executive Board of Edmond and Lily Safra Center for Brain Sciences, Hebrew University
Director, Swartz Theoretical Neuroscience Program, Harvard University Member, Steering Committee, Center for Brain Science, Harvard University Faculty, Methods in Computational Neuroscience Course, Woods Hole Member, USA Society for Neuroscience

Associate Editor, Neural Computation
Member, Israel Society for Neuroscience
Member, Federation of European Neuroscience Societies
Founding member of the Interdisciplinary Center for Neural Computation (ICNC), Hebrew University
Director, ICNC
Member, International Review Committee of the Department of Physics, Swiss Federal Institute of Technology, Zurich
Faculty, Advanced Course in Computational Neuroscience, IBRO/FENS Member, Executive Board of ICNC

2011 Swartz Prize for Theoretical and Computational Neuroscience of the US Society for Neuroscience

2008 Landau Prize for Brain Science
2008 Foreign Honorary Member of the American Academy of Arts and Sciences 2007 William N. Skirball Chair in Neuroscience, Hebrew University
2005 Hebrew University Presidential Award for Outstanding Researcher
1980 Rothschild Fellowship for Postdoctoral Research
1979 Chaim Weizmann Post-Doctoral Fellowship
1977 Sir Isaac Wolfson Prize for Excellence in Doctoral Research, Bar-Ilan University 1974 Bar-Ilan University Fellowship for Excellence
1973 Landau Prize for Outstanding M.Sc. Research
1972 Spiers Prize for Best Undergraduate Student, Bar-Ilan University