Haim Sompolinsky

发表

Object manifold geometry across the mouse cortical visual hierarchy

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

Nonlinear Population Codes

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

The Effect of Correlations on the Fisher Information of Population Codes

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

Separability and geometry of object manifolds in deep neural networks

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

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

Haim Sompolinsky, J. W. Kim, 1998 .

Editorial overview: Theoretical and computational neuroscience

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

Learning Precisely Timed Spikes

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

Separability and geometry of object manifolds in deep neural networks

Uri Cohen, Haim Sompolinsky, Daniel D. Lee, 2020, Nature Communications.

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

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

25th Annual Computational Neuroscience Meeting: CNS-2016

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

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

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

The European Future Technologies Conference and Exhibition 2011 Introducing the Human Brain Project

Alois Knoll, Thomas Lippert, Haim Sompolinsky, 2011 .

Optimal Architectures in a Solvable Model of Deep Networks

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

Computational neuroscience: beyond the local circuit

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

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

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

Adaptation and information transmission in fly motion detection.

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

Associative network models for central pattern generators

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

STATISTICAL MECHANICS OF NEURAL NETWORKS

Haim Sompolinsky, H. Sompolinsky, 1988 .

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.

On-line Learning of Dichotomies

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

Chaotic Balanced State in a Model of Cortical Circuits

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

Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

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

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.

Correlation Functions in a Large Stochastic Network

Haim Sompolinsky, Iris Ginzburg, 1993, NIPS.

Learning Data Manifolds with a Cutting Plane Method

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

Linear readout of object manifolds.

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

Poster: Low dimensional structures in local variability in a multi-scale neural circuit model

Haim Sompolinsky, Yu Hu, 2016 .

Input-dependent Suppression of Chaos in Recurrent Neural Networks

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

Neural Coding and Decoding with Spike Times

Haim Sompolinsky, Ran Rubin, Robert Gütig, 2013 .

Learning curves in large neural networks

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

Functional diversity among sensory neurons from efficient coding principles

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

Tempotron Learning

Haim Sompolinsky, Robert Gütig, 2014, Encyclopedia of Computational Neuroscience.

Classification of Manifolds by Single-Layer Neural Networks pdf

Daniel D. Lee, Haim Sompolinsky, SueYeon Chung, 2015, ArXiv.

A new role for circuit expansion for learning in neural networks pdf

Haim Sompolinsky, Madhu Advani, Julia Steinberg, 2020, ArXiv.

Processing of Sensory Information by a Network of Oscillators with Memory

Haim Sompolinsky, Michail Tsodyks, 1992, Int. J. Neural Syst..

Predicting the outputs of finite networks trained with noisy gradients pdf

Haim Sompolinsky, Oded Ben-David, Zohar Ringel, 2020, ArXiv.

Correlation Codes in Neuronal Populations

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

Functional diversity among sensory neurons from efficient coding principles

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

Bayesian Methods for Adaptive Models

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

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics

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

Introducing the Human Brain Project

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

Markov Transitions between Attractor States in a Recurrent Neural Network

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

Coherent chaos in a recurrent neural network with structured connectivity

Haim Sompolinsky, Itamar Daniel Landau, 2018, PLoS Comput. Biol..

Stimulus-dependent suppression of intrinsic variability in recurrent neural networks

Haim Sompolinsky, Laurence F Abbott, Kanaka Rajan, 2010, BMC Neuroscience.

Segmentation by a Network of Oscillators with Stored Memories

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

The Theory of Neural Networks: The Hebb Rule and Beyond

Haim Sompolinsky, H. Sompolinsky, 1987 .

Short-term memory in neuronal networks through dynamical compressed sensing

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

Learning a Continuous Hidden Variable Model for Binary Data

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

Evidence of Change of Intention in Picking Situations

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

Query by committee

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

Bayesian model of dynamic image stabilization in the visual system

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

Stimulus-Dependent Synchronization of Neuronal Assemblies

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

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

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

Functional diversity among sensory neurons from efficient coding principles

Haim Sompolinsky, Julijana Gjorgjieva, Markus Meister, 2019 .

Learning from Examples in a Single-Layer Neural Network

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

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

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

Classification and Geometry of General Perceptual Manifolds pdf

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

How the Brain Generates Movement

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

Neural Network Models of Perceptual Learning of Angle Discrimination

Haim Sompolinsky, Germán Mato, 1996, Neural Computation.

Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons

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

New perspectives on the mechanisms for orientation

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

Random input expansion improves classifier accuracy

Haim Sompolinsky, Madhu Advani, Julia Steinberg, 2019 .

Implications of Neuronal Diversity on Population Coding

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

Memory traces in dynamical systems

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

Rate Models for Conductance-Based Cortical Neuronal Networks

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

Statistical mechanics of compressed sensing.

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

An Information Maximization Approach to Overcomplete and Recurrent Representations

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

Short-term memory in orthogonal neural networks.

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

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

Haim Sompolinsky, Markus Meister, Uri Rokni, 2011, Proceedings of the National Academy of Sciences.

Advances in large margin classifiers

Manfred Opper, Haim Sompolinsky, Rainer Dietrich, 2000 .

Algorithms for Independent Components Analysis and Higher Order Statistics

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

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

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

A new role for circuit expansion for learning in neural networks

Haim Sompolinsky, Madhu Advani, Julia Steinberg, 2021, Physical review. E.

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

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

Neural networks with low local firing rates

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

Course 9 - Irregular Activity in Large Networks of Neurons

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

Localization in a disordered elastic medium near two dimensions

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

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

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

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

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

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

Haim Sompolinsky, Yonatan Loewenstein, Michael Häusser, 2006, Nature Neuroscience.

Theory of spike timing-based neural classifiers.

Rémi Monasson, Haim Sompolinsky, Ran Rubin, 2010, Physical review letters.

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

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

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

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

Dynamic Theory of the Spin Glass Phase

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

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 .

The spectrum of covariance matrices of randomly connected recurrent neuronal networks

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

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.

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

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

Optimal Degrees of Synaptic Connectivity

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

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

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

Irregular firing in cortical circuits with inhibition/excitation balance

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

Change of Intention in “Picking” Situations

Haim Sompolinsky, Leon Y. Deouell, Ariel Furstenberg, 2014 .

Erratum: Population coding in neuronal systems with correlated noise [Phys. Rev. E 64, 051904 (2001)]

Haim Sompolinsky, Maoz Shamir, Hyoungsoo Yoon, 2002 .

Time-Dependent Order Parameters in Spin-Glasses

Haim Sompolinsky, H. Sompolinsky, 1981 .

Cortical Representation of Bimanual Movements

Haim Sompolinsky, Eilon Vaadia, Uri Rokni, 2003, The Journal of Neuroscience.