H. Sompolinsky

发表

On-line Learning of Dichotomies: Algorithms and Learning Curves.

Haim Sompolinsky, N. Barkai, S SeungH, 1995, NIPS 1995.

Theory of orientation tuning in visual cortex.

H. Sompolinsky, R. Ben-Yishai, R. Bar-Or, 1995, Proceedings of the National Academy of Sciences of the United States of America.

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

H. Sompolinsky, S. Ganguli, 2012, Annual review of neuroscience.

Equilibrium properties of temporally asymmetric Hebbian plasticity.

Daniel D. Lee, H. Sompolinsky, J. Rubin, 2000, Physical review letters.

Statistical mechanics of neural networks near saturation

D. Amit, H. Gutfreund, H. Sompolinsky, 1987 .

Learning from Examples in a Single-Layer Neural Network

Haim Sompolinsky, David Hansel, H. Sompolinsky, 1990 .

Learning curves in large neural networks

H. Sebastian Seung, Naftali Tishby, Haim Sompolinsky, 1991, COLT '91.

Learning in a Two-Layer Neural Network of Edge Detectors

Naftali Tishby, H. Sompolinsky, 1990 .

Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity

H. Sompolinsky, C. van Vreeswijk, K. Moffat, 1996, Science.

Stimulus-dependent suppression of chaos in recurrent neural networks.

L. Abbott, H. Sompolinsky, Kanaka Rajan, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

H. Sompolinsky, R. Gütig, 2006, Nature Neuroscience.

Query by committee

H. Sebastian Seung, Manfred Opper, Haim Sompolinsky, 1992, COLT '92.

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

H. Sompolinsky, A. Zippelius, 1982 .

On-line Learning of Dichotomies

H. Sebastian Seung, Haim Sompolinsky, N. Barkai, 1994, NIPS.

New perspectives on the mechanisms for orientation selectivity

R. Shapley, H. Sompolinsky, 1997, Current Opinion in Neurobiology.

Transition to chaos in random neuronal networks

H. Sompolinsky, Jonathan Kadmon, 2015, 1508.06486.

Bayesian Methods for Adaptive Models

Peter Cheeseman, Radford M. Neal, Haim Sompolinsky, 2011 .

Bistability of cerebellar Purkinje cells modulated by sensory stimulation

H. Sompolinsky, M. Häusser, S. Mahon, 2005, Nature Neuroscience.

Statistical mechanics of Support Vector networks.

M. Opper, H. Sompolinsky, R. Dietrich, 1998, cond-mat/9811421.

An Information Maximization Approach to Overcomplete and Recurrent Representations

Daniel D. Lee, Oren Shriki, Haim Sompolinsky, 2000, NIPS.

Path integral approach to random neural networks

A. Crisanti, H. Sompolinsky, H. Sompolinksy, 2018, Physical Review E.

Coherent chaos in a recurrent neural network with structured connectivity

H. Sompolinsky, I. Landau, 2018, bioRxiv.

13 Modeling Feature Selectivity in Local Cortical Circuits

H. Sompolinsky, D. Hansel, 2022 .

Markov Transitions between Attractor States in a Recurrent Neural Network

David Rolnick, Haim Sompolinsky, Jeremy Bernstein, 2017, AAAI Spring Symposia.

Sparseness and Expansion in Sensory Representations

H. Sompolinsky, B. Babadi, 2014, Neuron.

Time-Warp–Invariant Neuronal Processing

H. Sompolinsky, R. Gütig, 2009, PLoS biology.

Memory traces in dynamical systems

Surya Ganguli, Haim Sompolinsky, Dongsung Huh, 2008, Proceedings of the National Academy of Sciences.

Temporal integration by calcium dynamics in a model neuron

H. Sompolinsky, Y. Loewenstein, 2003, Nature Neuroscience.

Graph optimisation problems and the Potts glass

H. Sompolinsky, I. Kanter, 1987 .

On the Transmission of Rate Code in Long Feedforward Networks with Excitatory–Inhibitory Balance

Idan Segev, H. Sompolinsky, M. Abeles, 2003, The Journal of Neuroscience.

Short-term memory in orthogonal neural networks.

Haim Sompolinsky, Daniel D Lee, Olivia L White, 2004, Physical review letters.

Associative network models for central pattern generators

Haim Sompolinsky, David Kleinfeld, D. Kleinfeld, 1989 .

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

H Sompolinsky, D Kleinfeld, D. Kleinfeld, 1988, Biophysical journal.

Population coding in neuronal systems with correlated noise.

H. Sompolinsky, K. Kang, H. Yoon, 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

Information Tuning of Populations of Neurons in Primary Visual Cortex

R. Shapley, H. Sompolinsky, K. Kang, 2004, The Journal of Neuroscience.

Neural mechanisms of speech processing : time warp invariance by adaptive integration time

H. Sompolinsky, R. Gütig, 2007 .

Spin-glass models of neural networks

D. Amit, H. Gutfreund, H. Sompolinsky, 1987 .

Mutual information of population codes and distance measures in probability space.

H. Sompolinsky, K. Kang, 2001, Physical review letters.

Statistical mechanics of compressed sensing.

Surya Ganguli, Haim Sompolinsky, H. Sompolinsky, 2010, Physical review letters.

Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks

Haim Sompolinsky, L F Abbott, Kanaka Rajan, 2009, 0912.3832.

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

H. Sompolinsky, M. Oberlaender, V. J. Dercksen, 2016, Neuron.

A Neural Computation for Visual Acuity in the Presence of Eye Movements

H. Sompolinsky, M. Meister, Xaq Pitkow, 2007, PLoS biology.

Benefits of Pathway Splitting in Sensory Coding

H. Sompolinsky, M. Meister, J. Gjorgjieva, 2014, The Journal of Neuroscience.

Selectivity and Sparseness in Randomly Connected Balanced Networks

H. Sompolinsky, C. Pehlevan, 2014, PloS one.

Course 9 - Irregular Activity in Large Networks of Neurons

Haim Sompolinsky, C. van Vreeswijk, H. Sompolinsky, 2005 .

Mexican hats and pinwheels in visual cortex

H. Sompolinsky, M. Shelley, K. Kang, 2003, Proceedings of the National Academy of Sciences of the United States of America.

Oscillations by symmetry breaking in homogeneous networks with electrical coupling.

Haim Sompolinsky, Yonatan Loewenstein, H. Sompolinsky, 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

The spectrum of covariance matrices of randomly connected recurrent neuronal networks

Haim Sompolinsky, Yu Hu, H. Sompolinsky, 2020, bioRxiv.

Computing Complex Visual Features with Retinal Spike Times

H. Sompolinsky, M. Meister, R. Gütig, 2013, PloS one.

Adaptation without parameter change: Dynamic gain control in motion detection

H. Sompolinsky, A. Borst, V. Flanagin, 2005, Proceedings of the National Academy of Sciences of the United States of America.

Optimal Degrees of Synaptic Connectivity

Haim Sompolinsky, Kameron Decker Harris, Ashok Litwin-Kumar, 2017, Neuron.

Brainwide organization of neuronal activity and convergent sensorimotor transformations in larval zebrafish

Haim Sompolinsky, Florian Engert, Misha B. Ahrens, 2018, bioRxiv.

Correlation Functions in a Large Stochastic Neural Network

H. Sompolinsky, I. Ginzburg, 1993, NIPS 1993.

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

H. Sompolinsky, Ravid Shwartz-Ziv, A. Mizrahi, 2018, bioRxiv.

Time-Dependent Order Parameters in Spin-Glasses

Haim Sompolinsky, H. Sompolinsky, 1981 .

Effect of Aging on Change of Intention

H. Sompolinsky, R. Knight, L. Deouell, 2019, Front. Hum. Neurosci..

A study of short-range spin glasses

H. Sompolinsky, A. Zippelius, 1983 .

Title: Generalized Synaptic Updating in Temporally Asymmetric Hebbian Learning Authors:

H. Sompolinsky, A. Aertsen, S. Rotter, 2001 .

Object manifold geometry across the mouse cortical visual hierarchy

Emmanouil Froudarakis, Uri Cohen, Maria Diamantaki, 2020, bioRxiv.

Evidence of Change of Intention in Picking Situations

Haim Sompolinsky, Assaf Breska, Leon Y. Deouell, 2015, Journal of Cognitive Neuroscience.

Scaling behavior of diffusion on percolation clusters

Haim Sompolinsky, Shlomo Havlin, Daniel ben-Avraham, 1983 .

Stable Orientation Tuning in the Visual Cortex.

Haim Sompolinsky, H. Sompolinsky, A Heimal, 2000 .

Synchrony in Integrate-and-Fire Networks

H. Sompolinsky, M. Tsodyks, I. Mitkov, 1993 .

Temporal support vectors for spiking neuronal networks

H. Sompolinsky, Ran Rubin, 2022, 2205.14544.

Young adult-born neurons improve odor coding by mitral cells

H. Sompolinsky, I. Landau, A. Mizrahi, 2020, Nature Communications.

A theory of learning with constrained weight-distribution

Daniel D. Lee, H. Sompolinsky, Ben Sorscher, 2022, ArXiv.

Soft-margin classification of object manifolds

H. Sompolinsky, Uri Cohen, 2022, Physical review. E.

Short-term memory in orthogonal neural networks.

Daniel D. Lee, H. Sompolinsky, O. White, 2004, Physical review letters.

Neural representational geometry underlies few-shot concept learning

H. Sompolinsky, S. Ganguli, Ben Sorscher, 2022, Proceedings of the National Academy of Sciences of the United States of America.

A Minimum Perturbation Theory of Deep Perceptual Learning

H. Sompolinsky, H. Shan, 2021, bioRxiv.

Statistical mechanics of Support Vector networks.

M. Opper, H. Sompolinsky, R. Dietrich, 1998, cond-mat/9811421.

A theory of learning with constrained weight-distribution

Daniel D. Lee, H. Sompolinsky, Ben Sorscher, 2022, ArXiv.

Globally Gated Deep Linear Networks

H. Sompolinsky, Qianyi Li, 2022, 2210.17449.

Globally Gated Deep Linear Networks

H. Sompolinsky, Qianyi Li, 2022, NeurIPS.

Slow adaptation facilitates excitation-inhibition balance in the presence of structural heterogeneity

H. Sompolinsky, M. Oberlaender, V. J. Dercksen, 2016 .

The relationship between microcircuit structure and dynamics in the rat barrel cortex

H. Sompolinsky, M. Oberlaender, R. Egger, 2014 .

Neural Coding and Decoding with Spike Times

Haim Sompolinsky, Ran Rubin, Robert Gütig, 2013 .

Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

Haim Sompolinsky, S Rotter, R Gütig, 2003, The Journal of Neuroscience.

Dynamics of random neural networks with bistable units.

H Sompolinsky, M Stern, L F Abbott, 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

STATISTICAL MECHANICS OF NEURAL NETWORKS

Haim Sompolinsky, H. Sompolinsky, 1988 .

Dynamic Theory of the Spin Glass Phase

Haim Sompolinsky, Annette Zippelius, H. Sompolinsky, 1981 .

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

Haim Sompolinsky, David Hansel, H. Sompolinsky, 1996, Journal of Computational Neuroscience.

New perspectives on the mechanisms for orientation

Haim Sompolinsky, Robert Shapleyt, H. Sompolinsky, 1997 .

The Olivo-Cerebellar System : Dynamical Processes and Computational Principles

H. Sompolinsky, Y. Loewenstein, Y. Yarom, 2003 .

Low-frequency dynamics of KH2PO4-type crystals

H. Sompolinsky, S. Havlin, 1979 .

The anomalous temperature dependence of the protonic E mode in kdp-type crystals

H. Sompolinsky, S. Havlin, E. Litov, 1975 .

Central E-mode and transverse dynamical properties of KDP-type crystals

H. Sompolinsky, S. Havlin, 1976 .

Theoretical and experimental studies of the transverse dielectric properties of K D 2 P O 4

H. Sompolinsky, S. Havlin, E. Litov, 1976 .

Anisotropic polarisation fluctuations in KD2PO4-type crystals

H. Sompolinsky, S. Havlin, R. Cowley, 1982 .

Cluster Approximation for Q-dependent Correlations in Magnetic and Ferroelectric Systems

H. Sompolinsky, S. Havlin, 1982 .

Wave vector dependent susceptibility of KD2PO4 in the four cluster approximation

H. Sompolinsky, S. Havlin, 1981 .

Fluctuations in Short-Range Spin-Glasses

H. Sompolinsky, A. Zippelius, 1983 .

Learning Data Manifolds with a Cutting Plane Method

Daniel D. Lee, Haim Sompolinsky, Uri Cohen, 2017, Neural Computation.

Phase Transition in a Dzyaloshinsky-Moriya Spin-Glass

H. Sompolinsky, G. Kotliar, 1984 .

Algorithms for Independent Components Analysis and Higher Order Statistics

Daniel D. Lee, Haim Sompolinsky, Uri Rokni, 1999, NIPS.

25th Annual Computational Neuroscience Meeting: CNS-2016

Morten L. Kringelbach, Majid Nili Ahmadabadi, Anders M. Dale, 2016, BMC Neuroscience.

Computational neuroscience: beyond the local circuit

Haim Sompolinsky, H. Sompolinsky, 2014, Current Opinion in Neurobiology.

Course 8 – Theory of Large Recurrent Networks: From Spikes to Behavior

H. Sompolinsky, O. L. White, H. Sompolinsky, 2005 .

Patterns of Ongoing Activity and the Functional Architecture of the Primary Visual Cortex

Haim Sompolinsky, Joshua A. Goldberg, Uri Rokni, 2004, Neuron.

Rate Models for Conductance-Based Cortical Neuronal Networks

Oren Shriki, Haim Sompolinsky, David Hansel, 2003, Neural Computation.

Chaotic Balanced State in a Model of Cortical Circuits

Haim Sompolinsky, Carl van Vreeswijk, H. Sompolinsky, 1998, Neural Computation.

Locally ordered representation of 3D space in the entorhinal cortex

H. Sompolinsky, N. Ulanovsky, Liora Las, 2021, Nature.

Associative memory of structured knowledge

Julia Steinberg, Haim Sompolinsky, H. Sompolinsky, 2020, bioRxiv.

Learning a Continuous Hidden Variable Model for Binary Data

Daniel D. Lee, Haim Sompolinsky, Daniel D. Lee, 1998, NIPS.

Adaptation and information transmission in fly motion detection.

Alexander Borst, Haim Sompolinsky, Moshe N Safran, 2007, Journal of neurophysiology.

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

H Sompolinsky, D Golomb, D Kleinfeld, 1990, Proceedings of the National Academy of Sciences of the United States of America.

Separability and geometry of object manifolds in deep neural networks

Haim Sompolinsky, Uri Cohen, Sueyeon Chung, 2019, Nature Communications.

Localization in a disordered elastic medium near two dimensions

Haim Sompolinsky, H. Sompolinsky, S. John, 1983 .

Traveling Waves and the Processing of Weakly Tuned Inputs in a Cortical Network Module

Haim Sompolinsky, David Hansel, Rani Ben-Yishai, 2004, Journal of Computational Neuroscience.

Stimulus-Dependent Synchronization of Neuronal Assemblies

Haim Sompolinsky, E. R. Grannan, D. Kleinfeld, 1993, Neural Computation.

Bayesian model of dynamic image stabilization in the visual system

Haim Sompolinsky, Markus Meister, Uri Rokni, 2010, Proceedings of the National Academy of Sciences.

Introducing the Human Brain Project

Karlheinz Meier, Alois Knoll, Richard S. Frackowiak, 2011, FET.

Correlation Codes in Neuronal Populations

Haim Sompolinsky, Maoz Shamir, H. Sompolinsky, 2001, NIPS.

Implications of Neuronal Diversity on Population Coding

Haim Sompolinsky, Maoz Shamir, H. Sompolinsky, 2006, Neural Computation.

Nonlinear Population Codes

Haim Sompolinsky, Maoz Shamir, H. Sompolinsky, 2004, Neural Computation.

Spin-resonance frequencies in spin-glasses with random anisotropies

H. Sompolinsky, B. Halperin, C. Henley, 1982 .

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics

Haim Sompolinsky, L. F. Abbott, Kanaka Rajan, 2010, NIPS.

Abstracts of the 16th Annual Meeting of The Israel Society for Neuroscience Eilat, Israel, November 25–27, 2007

Yonina C. Eldar, Karl J. Friston, Leslie G. Ungerleider, 2008, Neural plasticity.

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Haim Sompolinsky, Florian Engert, Timothy W. Dunn, 2016, Cell.

Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons

Haim Sompolinsky, Yoram Burak, Sam Lewallen, 2009, Neural Computation.

Learning Precisely Timed Spikes

Haim Sompolinsky, Raoul-Martin Memmesheimer, Ran Rubin, 2014, Neuron.

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

Haim Sompolinsky, Florian Engert, Misha B. Ahrens, 2018, Neuron.

Functional diversity among sensory neurons from efficient coding principles

Haim Sompolinsky, Julijana Gjorgjieva, Markus Meister, 2019, bioRxiv.

The Theory of Neural Networks: The Hebb Rule and Beyond

Haim Sompolinsky, H. Sompolinsky, 1987 .

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

H. Sompolinsky, A. Zippelius, 1982 .

The generation of oscillations in networks of electrically coupled cells

H Sompolinsky, H. Sompolinsky, Y. Loewenstein, 2001, Proceedings of the National Academy of Sciences of the United States of America.

Stochastic consolidation of lifelong memory

H. Sompolinsky, Nimrod Shaham, Gabriel Kreiman, 2021, Scientific Reports.

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

Haim Sompolinsky, Qianyi Li, H. Sompolinsky, 2020, Physical Review X.

Brain-wide Organization o f Neuronal Activity and Convergent Sensorimotor Transformations in Larval Zebrafish Highlights

Aaron T. Kuan, Alex B. Chen, F. Engert, 2018 .

Irregular firing in cortical circuits with inhibition/excitation balance

Haim Sompolinsky, C. van Vreeswijk, H. Sompolinsky, 1997 .

Simple models for reading neuronal population codes.

H Sompolinsky, H S Seung, Richard C. Wilson, 1993, Proceedings of the National Academy of Sciences of the United States of America.

Neural networks with low local firing rates

Nava Rubin, Haim Sompolinsky, H. Sompolinsky, 1989 .

Classification and Geometry of General Perceptual Manifolds pdf

Daniel D. Lee, Haim Sompolinsky, SueYeon Chung, 2017, Physical Review X.

The Geometry of Concept Learning

H. Sompolinsky, S. Ganguli, Ben Sorscher, 2021, bioRxiv.

Short-term memory in neuronal networks through dynamical compressed sensing

Surya Ganguli, Haim Sompolinsky, H. Sompolinsky, 2010, NIPS.

Object manifold geometry across the mouse cortical visual hierarchy

Edgar Y. Walker, H. Sompolinsky, Philipp Berens, 2020, bioRxiv.

Linear readout of object manifolds.

Haim Sompolinsky, Daniel D Lee, SueYeon Chung, 2015, Physical review. E.

Abstracts of the 16th Annual Meeting of The Israel Society for Neuroscience Eilat, Israel, November 25–27, 2007

Yonina C. Eldar, Karl J. Friston, Leslie G. Ungerleider, 2008, Neural plasticity.

Editorial overview: Theoretical and computational neuroscience

Haim Sompolinsky, Adrienne Fairhall, H. Sompolinsky, 2014, Current Opinion in Neurobiology.

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

Haim Sompolinsky, L. F. Abbott, Ran Rubin, 2017, Proceedings of the National Academy of Sciences.

Early perceptual loss in depression

Yonina C. Eldar, Karl J. Friston, Charles R. Seger, 2007 .

in Alert Monkeys Response Magnitude and Receptive Field Geometry Eye Position Compensation Improves Estimates of

A. Saul, H. Sompolinsky, D. Snodderly, 2007 .

Neural Correlates of Learning Pure Tones or Natural Sounds in the Auditory Cortex

Haim Sompolinsky, Adi Mizrahi, Ido Maor, 2020, Frontiers in Neural Circuits.

Segmentation by a Network of Oscillators with Stored Memories

Haim Sompolinsky, Michail Tsodyks, H. Sompolinsky, 1994, Neural Computation.

Abstracts of the 15th Annual Meeting of the Israel Society for Neuroscience Eilat, Israel, December 3–5, 2006

Leslie G. Ungerleider, D. Amit, H. Seung, 2006, Neural Plasticity.

Predicting the outputs of finite networks trained with noisy gradients

H. Sompolinsky, Z. Ringel, Oded Ben-David, 2020, ArXiv.

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

Haim Sompolinsky, Zohar Ringel, Gadi Naveh, 2020, Physical review. E.

Optimal Architectures in a Solvable Model of Deep Networks

Jonathan Kadmon, Haim Sompolinsky, H. Sompolinsky, 2016, NIPS.

Functional diversity among sensory neurons from efficient coding principles

Haim Sompolinsky, Julijana Gjorgjieva, Markus Meister, 2017, bioRxiv.

Chaos in random neural networks.

Sommers, Sompolinsky, A. Crisanti, 1988, Physical review letters.

Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Renormalization Group pdf

Qianyi Li, Haim Sompolinsky, H. Sompolinsky, 2020, ArXiv.

Temporal association in asymmetric neural networks.

Kanter, Sompolinsky, H. Sompolinsky, 1986, Physical review letters.

Advances in large margin classifiers

Manfred Opper, Haim Sompolinsky, Rainer Dietrich, 2000 .

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

Sompolinsky, Gutfreund, D. Amit, 1985, Physical review letters.

Statistical mechanics of learning from examples.

Sompolinsky, Tishby, Seung, 1992, Physical review. A, Atomic, molecular, and optical physics.

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

Kanter, Sompolinsky, H. Sompolinsky, 1987, Physical review letters.

Spin-glass models of neural networks.

Sompolinsky, Gutfreund, D. Amit, 1985, Physical review. A, General physics.

The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics

H. Sompolinsky, Yu Hu, 2022, PLoS Comput. Biol..

The Effect of Correlations on the Fisher Information of Population Codes

Haim Sompolinsky, Hyoungsoo Yoon, H. Sompolinsky, 1998, NIPS.

Theory of correlations in stochastic neural networks.

Sompolinsky, H. Sompolinsky, Ginzburg, 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

Spectrum of large random asymmetric matrices.

Sommers, Sompolinsky, A. Crisanti, 1988, Physical review letters.

Spin-glass transition in a magnetic field

H. Sompolinsky, 1985 .

Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime

H. Sompolinsky, Qianyi Li, Yehonatan Avidan, 2023, ArXiv.

How the Brain Generates Movement

Haim Sompolinsky, Uri Rokni, H. Sompolinsky, 2012, Neural Computation.

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

H. Sompolinsky, C. Dasgupta, 1983 .

Exchange Stiffness and Macroscopic Anisotropy in Heisenberg Spin-Glasses

H. Sompolinsky, A. Zippelius, G. Kotliar, 1984 .

Irreversible response in spin-glasses: An experimental study in amorphous Fe-Mn

H. Sompolinsky, Y. Yeshurun, 1982 .

On the 'naive' mean-field equations for spin glasses

Haim Sompolinsky, H. Sompolinsky, A. Bray, 1986 .

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

Sompolinsky, H. Sompolinsky, N. Rubin, 1990, Physical review. A, Atomic, molecular, and optical physics.

Neural networks with nonlinear synapses and a static noise.

Sompolinsky, H. Sompolinsky, 1986, Physical review. A, General physics.

Clustering in globally coupled phase oscillators.

Hansel, Sompolinsky, H. Sompolinsky, 1992, Physical review. A, Atomic, molecular, and optical physics.

Cooperative dynamics in visual processing.

Sompolinsky, D. Kleinfeld, H. Sompolinsky, 1991, Physical review. A, Atomic, molecular, and optical physics.

Purkinje cells in awake behaving animals operate at the upstate membrane potential

H. Sompolinsky, M. Häusser, Y. Loewenstein, 2006, Nature Neuroscience.

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

Adi Mizrahi, Ido Maor, Yishai M Elyada, 2018, bioRxiv.

Tempotron Decoding of Spike Patterns from Retinal Ganglion Cell Populations

H. Sompolinsky, M. Meister, R. Gütig, 2007 .

Information Tuning of Populations of Neurons in Primary Visual Cortex

R. Shapley, H. Sompolinsky, Kukjin Kang, 2022 .

A Hebbian learning rule mediates asymmetric plasticity in aligning sensory representations.

Haim Sompolinsky, Eric I Knudsen, Ilana B. Witten, 2008, Journal of neurophysiology.

Minimum perturbation theory of deep perceptual learning.

H. Sompolinsky, H. Shan, 2022, Physical review. E.

Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions.

Sompolinsky, H. Sompolinsky, M. Tsodyks, 1993, Physical review letters.

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

Sompolinsky, A. Crisanti, H. Sompolinsky, 1988, Physical review. A, General physics.

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

Sompolinsky, A. Crisanti, H. Sompolinsky, 1987, Physical review. A, General physics.

Associative recall of memory without errors.

Kanter, Sompolinsky, H. Sompolinsky, 1987, Physical review. A, General physics.

Mean-field theory of the Potts glass.

Kanter, Sompolinsky, H. Sompolinsky, 1985, Physical review letters.

The wavevector dependence of the polarization fluctuations in Kd2PO4

H. Sompolinsky, S. Havlin, R. Cowley, 1980 .