Haim Sompolinsky

According to our database, Haim Sompolinsky authored at least 50 papers between 1991 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit.

Zechen Zhang, Haim Sompolinsky

CoRR, 2024

Order parameters and phase transitions of continual learning in deep neural networks.

Haozhe Shan, Qianyi Li, Haim Sompolinsky

CoRR, 2024

Coding schemes in neural networks learning classification tasks.

Alexander van Meegen, Haim Sompolinsky

CoRR, 2024

Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers.

Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky

CoRR, 2024

2023

Optimal Quadratic Binding for Relational Reasoning in Vector Symbolic Neural Architectures.

Naoki Hiratani, Haim Sompolinsky

Neural Comput., February, 2023

Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds.

Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung

CoRR, 2023

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

Yehonatan Avidan, Qianyi Li, Haim Sompolinsky

CoRR, 2023

2022

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

Yu Hu, Haim Sompolinsky

PLoS Comput. Biol., 2022

A theory of learning with constrained weight-distribution.

Weishun Zhong, Ben Sorscher, Daniel D. Lee, Haim Sompolinsky

CoRR, 2022

Soft-margin classification of object manifolds.

Uri Cohen, Haim Sompolinsky

CoRR, 2022

A theory of weight distribution-constrained learning.

Weishun Zhong, Ben Sorscher, Daniel Lee, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Globally Gated Deep Linear Networks.

Qianyi Li, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

High-dimensional dynamics of generalization error in neural networks.

Madhu S. Advani, Andrew M. Saxe, Haim Sompolinsky

Neural Networks, 2020

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

Qianyi Li, Haim Sompolinsky

CoRR, 2020

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

Julia Steinberg, Madhu Advani, Haim Sompolinsky

CoRR, 2020

Predicting the outputs of finite networks trained with noisy gradients.

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

CoRR, 2020

2019

Functional diversity among sensory neurons from efficient coding principles.

Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky

PLoS Comput. Biol., 2019

2018

Coherent chaos in a recurrent neural network with structured connectivity.

Itamar Daniel Landau, Haim Sompolinsky

PLoS Comput. Biol., 2018

Learning Data Manifolds with a Cutting Plane Method.

SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee

Neural Comput., 2018

2017

Classification and Geometry of General Perceptual Manifolds.

SueYeon Chung, Daniel D. Lee, Haim Sompolinsky

CoRR, 2017

Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity.

Ran Rubin, L. F. Abbott, Haim Sompolinsky

CoRR, 2017

Markov Transitions between Attractor States in a Recurrent Neural Network.

Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky

Proceedings of the 2017 AAAI Spring Symposia, 2017

2016

Optimal Architectures in a Solvable Model of Deep Networks.

Jonathan Kadmon, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Evidence of Change of Intention in Picking Situations.

Ariel Furstenberg, Assaf Breska, Haim Sompolinsky, Leon Y. Deouell

J. Cogn. Neurosci., 2015

Classification of Manifolds by Single-Layer Neural Networks.

SueYeon Chung, Daniel D. Lee, Haim Sompolinsky

CoRR, 2015

2014

Tempotron Learning.

Robert Gütig, Haim Sompolinsky

Proceedings of the Encyclopedia of Computational Neuroscience, 2014

2012

How the Brain Generates Movement.

Uri Rokni, Haim Sompolinsky

Neural Comput., 2012

2011

Introducing the Human Brain Project.

Henry Markram, Karlheinz Meier, Thomas Lippert, Sten Grillner, Richard S. Frackowiak, Stanislas Dehaene, Alois C. Knoll, Haim Sompolinsky, Kris Verstreken, Javier DeFelipe, Seth Grant, Jean-Pierre Changeux, Alois Saria

Proceedings of the 2nd European Future Technologies Conference and Exhibition, 2011

2010

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics.

Kanaka Rajan, L. F. Abbott, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Short-term memory in neuronal networks through dynamical compressed sensing.

Surya Ganguli, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons.

Yoram Burak, Sam Lewallen, Haim Sompolinsky

Neural Comput., 2009

2006

Implications of Neuronal Diversity on Population Coding.

Maoz Shamir, Haim Sompolinsky

Neural Comput., 2006

2004

Nonlinear Population Codes.

Maoz Shamir, Haim Sompolinsky

Neural Comput., 2004

2003

Rate Models for Conductance-Based Cortical Neuronal Networks.

Oren Shriki, David Hansel, Haim Sompolinsky

Neural Comput., 2003

2001

Correlation Codes in Neuronal Populations.

Maoz Shamir, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000

An Information Maximization Approach to Overcomplete and Recurrent Representations.

Oren Shriki, Haim Sompolinsky, Daniel D. Lee

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

Algorithms for Independent Components Analysis and Higher Order Statistics.

Daniel D. Lee, Uri Rokni, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998

Chaotic Balanced State in a Model Of Cortical Circuits.

Carl van Vreeswijk, Haim Sompolinsky

Neural Comput., 1998

The Effect of Correlations on the Fisher Information of Population Codes.

Hyoungsoo Yoon, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Learning a Continuous Hidden Variable Model for Binary Data.

Daniel D. Lee, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

1997

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

Rani Ben-Yishai, David Hansel, Haim Sompolinsky

J. Comput. Neurosci., 1997

1996

Neural network models of perceptual learning of angle discrimination.

Germán Mato, Haim Sompolinsky

Neural Comput., 1996

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

David Hansel, Haim Sompolinsky

J. Comput. Neurosci., 1996

1994

Segmentation by a Network of Oscillators with Stored Memories.

Haim Sompolinsky, Michail Tsodyks

Neural Comput., 1994

On-line Learning of Dichotomies.

N. Barkai, H. Sebastian Seung, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1993

Stimulus-Dependent Synchronization of Neuronal Assemblies.

E. R. Grannan, D. Kleinfeld, Haim Sompolinsky

Neural Comput., 1993

Correlation Functions in a Large Stochastic Network.

Iris Ginzburg, Haim Sompolinsky

Proceedings of the Advances in Neural Information Processing Systems 6, 1993

1992

Processing of Sensory Information by a Network of Oscillators with Memory.

Haim Sompolinsky, Michail Tsodyks

Int. J. Neural Syst., 1992

Query by Committee.

H. Sebastian Seung, Manfred Opper, Haim Sompolinsky

Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992

1991

Learning Curves in Large Neural Networks.

H. Sebastian Seung, Haim Sompolinsky, Naftali Tishby

Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991