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.

Lectures

Yehonatan Avidan

PhD Student

Nadav Lederman

MSc Student

Yoni Ankri

MSc Student

Ariel Furstenberg

Nimrod Shaham

Uri Cohen

Gadi Mintz

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)

Ginosar G, Aljadeff J, Burak Y, Sompolinsky H, Las L, Ulanovsky N.

Locally ordered representation of 3D space in the entorhinal cortex

Nature (2021)

Advani MS, Saxe AM, Sompolinsky H.

High-dimensional dynamics of generalization error in neural networks

Neural Netw (2020)

Maor, I., Ziv, R., Feigin, L., Elyada Y., Sompolinski H., Mizrahi A.

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

Frontiers in Neural Circuits (2020)

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)

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)

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

Separability and geometry of object manifolds in deep neural networks

Nat Commun 11, 746 (2020)

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

Effect of Aging on Change of Intention

Frontiers in Human Neuroscience 13:264 (2019)

Gjorgjieva J, Meister M, Sompolinsky H

Functional diversity among sensory neurons from efficient coding principles

PLOS Computational Biology (2019)

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)

Itamar Daniel Landau, Haim Sompolinsky

Coherent chaos in a recurrent neural network with structured connectivity

PLOS Computational Biology (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)

A Crisanti, H Sompolinsky

Path integral approach to random neural networks

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

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)

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)

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 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)

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)

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)

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)

Haim Sompolinsky

Computational neuroscience: beyond the local circuit

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

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

Learning Precisely Timed Spikes

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

Cengiz Pehlevan; Haim Sompolinsky

Selectivity and Sparseness in Randomly Connected Balanced Networks

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

Baktash Babadi; Haim Sompolinsky

Sparseness and Expansion in Sensory Representations

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

Uri Rokni; Haim Sompolinsky

How the Brain Generates Movement

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

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)

Rajan, K; L F Abbott; H Sompolinsky

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics

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)

Ganguli. S, H. Sompolinsky

Short-term memory in neuronal networks through dynamical compressed sensing

Neural Information Processing Systems (2010) (2010)

Surya Ganguli; Haim Sompolinsky

Statistical mechanics of compressed sensing

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

Rubin R, Monasson R, Sompolinsky H.

Theory of spike timing-based neural classifiers

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

Yoram Burak, Sam Lewallen, Haim Sompolinsky

Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons

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

Gütig R, Sompolinsky H.

Time-warp-invariant neuronal processing

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

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)

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)

Pitkow X, Sompolinsky H, Meister M.

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

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

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

Adaptation and information transmission in fly motion detection

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

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

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)

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)

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)

Kang K, Shapley RM, Sompolinsky H.

Information tuning of populations of neurons in primary visual cortex

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

Maoz Shamir; Haim Sompolinsky

Nonlinear population codes

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

Olivia L White; Daniel D Lee; Haim Sompolinsky

Short-term memory in orthogonal neural networks

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

Uri Rokni; Orna Steinberg; Eilon Vaadia; Haim Sompolinsky

Cortical representation of bimanual movements

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

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)

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)

Oren Shriki; David Hansel; Haim Sompolinsky

Rate models for conductance-based cortical neuronal networks

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

Yonatan Loewenstein; Haim Sompolinsky

Temporal integration by calcium dynamics in a model neuron

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

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)

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)

Rubin J, Lee DD, Sompolinsky H.

Equilibrium properties of temporally asymmetric Hebbian plasticity

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

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)

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)

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)

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)

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)

Rainer Dietrich, Manfred Opper, and Haim Sompolinsky

Statistical Mechanics of Support Vector Networks

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

Van Vreeswijk C, Sompolinsky H.

Chaotic balanced state in a model of cortical circuits

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

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)

H Sompolinsky; Kim J.

On-line Gibbs learning. I. General Theory

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

Sompolinsky H, Shapley R.

New perspectives on the mechanisms for orientation selectivity

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

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)

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)

van Vreeswijk C, Sompolinsky H.

Chaos in neuronal networks with balanced excitatory and inhibitory activity

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

Mato G, Sompolinsky H.

Neural network models of perceptual learning of angle discrimination

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

Kim JW, Sompolinsky H.

On-line Gibbs learning

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

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)

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)

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)

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)

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)

Ginzburg I I, Sompolinsky H.

Theory of correlations in stochastic neural networks

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

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)

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)

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)

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)

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)

D. Hansel and H. Sompolinsky

Solvable model of spatiotemporal chaos

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

Haim Sompolinsky

Theoretical Issues in Learning from Examples

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

Barkai E, Hansel D, Sompolinsky H.

Broken symmetries in multilayered perceptrons

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

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)

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)

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, Sompolinsky H, Tishby N.

Statistical mechanics of learning from examples

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

D. Hansel and H. Sompolinsky

Synchronization and computation in a chaotic neural network

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

Sompolinsky H, Golomb D, Kleinfeld D.

Cooperative dynamics in visual processing

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

ELSC Program in Theoretical Neuroscience
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