Learning Curves in Large Neural Networks. - H. Sebastian Seung; Haim Sompolinsky; Naftali Tishby (COLT, 1991)

Query by Committee. - H. Sebastian Seung; Manfred Opper; Haim Sompolinsky (COLT, 1992)

Processing of Sensory Information by a Network of Oscillators with Memory. - Haim Sompolinsky; Michail Tsodyks (Int. J. Neural Syst., 1992)

Correlation Functions in a Large Stochastic Network. - Iris Ginzburg; Haim Sompolinsky (NIPS, 1993)

Stimulus-Dependent Synchronization of Neuronal Assemblies. - E. R. Grannan; D. Kleinfeld; Haim Sompolinsky (Neural Comput., 1993)

On-line Learning of Dichotomies. - N. Barkai; H. Sebastian Seung; Haim Sompolinsky (NIPS, 1994)

Segmentation by a Network of Oscillators with Stored Memories. - Haim Sompolinsky; Michail Tsodyks (Neural Comput., 1994)

Chaos and synchrony in a model of a hypercolumn in visual cortex. - David Hansel; Haim Sompolinsky (J. Comput. Neurosci., 1996)

Neural network models of perceptual learning of angle discrimination. - Germán Mato; Haim Sompolinsky (Neural Comput., 1996)

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)

Learning a Continuous Hidden Variable Model for Binary Data. - Daniel D. Lee; Haim Sompolinsky (NIPS, 1998)

The Effect of Correlations on the Fisher Information of Population Codes. - Hyoungsoo Yoon; Haim Sompolinsky (NIPS, 1998)

Chaotic Balanced State in a Model Of Cortical Circuits. - Carl van Vreeswijk; Haim Sompolinsky (Neural Comput., 1998)

Algorithms for Independent Components Analysis and Higher Order Statistics. - Daniel D. Lee; Uri Rokni; Haim Sompolinsky (NIPS, 1999)

An Information Maximization Approach to Overcomplete and Recurrent Representations. - Oren Shriki; Haim Sompolinsky; Daniel D. Lee (NIPS, 2000)

Correlation Codes in Neuronal Populations. - Maoz Shamir; Haim Sompolinsky (NIPS, 2001)

Rate Models for Conductance-Based Cortical Neuronal Networks. - Oren Shriki; David Hansel; Haim Sompolinsky (Neural Comput., 2003)

Nonlinear Population Codes. - Maoz Shamir; Haim Sompolinsky (Neural Comput., 2004)

Implications of Neuronal Diversity on Population Coding. - Maoz Shamir; Haim Sompolinsky (Neural Comput., 2006)

Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons. - Yoram Burak; Sam Lewallen; Haim Sompolinsky (Neural Comput., 2009)

Short-term memory in neuronal networks through dynamical compressed sensing. - Surya Ganguli; Haim Sompolinsky (NIPS, 2010)

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. - Kanaka Rajan; L. F. Abbott; Haim Sompolinsky (NIPS, 2010)

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; et al. (FET, 2011)

How the Brain Generates Movement. - Uri Rokni; Haim Sompolinsky (Neural Comput., 2012)

Tempotron Learning. - Robert Gütig; Haim Sompolinsky (Encyclopedia of Computational Neuroscience, 2014)

Classification of Manifolds by Single-Layer Neural Networks. - SueYeon Chung; Daniel D. Lee; Haim Sompolinsky (CoRR, 2015)

Evidence of Change of Intention in Picking Situations. - Ariel Furstenberg; Assaf Breska; Haim Sompolinsky; Leon Y. Deouell (J. Cogn. Neurosci., 2015)

Optimal Architectures in a Solvable Model of Deep Networks. - Jonathan Kadmon; Haim Sompolinsky (NIPS, 2016)

Markov Transitions between Attractor States in a Recurrent Neural Network. - Jeremy Bernstein; Ishita Dasgupta 001; David Rolnick; Haim Sompolinsky (AAAI Spring Symposia, 2017)

Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity. - Ran Rubin; L. F. Abbott; Haim Sompolinsky (CoRR, 2017)

Classification and Geometry of General Perceptual Manifolds. - SueYeon Chung; Daniel D. Lee; Haim Sompolinsky (CoRR, 2017)

Learning Data Manifolds with a Cutting Plane Method. - SueYeon Chung; Uri Cohen; Haim Sompolinsky; Daniel D. Lee (Neural Comput., 2018)

Coherent chaos in a recurrent neural network with structured connectivity. - Itamar Daniel Landau; Haim Sompolinsky (PLoS Comput. Biol., 2018)

Functional diversity among sensory neurons from efficient coding principles. - Julijana Gjorgjieva; Markus Meister; Haim Sompolinsky (PLoS Comput. Biol., 2019)

Predicting the outputs of finite networks trained with noisy gradients. - Gadi Naveh; Oded Ben-David; Haim Sompolinsky; Zohar Ringel (CoRR, 2020)

A new role for circuit expansion for learning in neural networks. - Julia Steinberg; Madhu Advani; Haim Sompolinsky (CoRR, 2020)

Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Renormalization Group. - Qianyi Li; Haim Sompolinsky (CoRR, 2020)

High-dimensional dynamics of generalization error in neural networks. - Madhu S. Advani; Andrew M. Saxe; Haim Sompolinsky (Neural Networks, 2020)

Globally Gated Deep Linear Networks. - Qianyi Li; Haim Sompolinsky (NeurIPS, 2022)

A theory of weight distribution-constrained learning. - Weishun Zhong; Ben Sorscher; Daniel Lee; Haim Sompolinsky (NeurIPS, 2022)

Soft-margin classification of object manifolds. - Uri Cohen; Haim Sompolinsky (CoRR, 2022)

A theory of learning with constrained weight-distribution. - Weishun Zhong; Ben Sorscher; Daniel D. Lee; Haim Sompolinsky (CoRR, 2022)

The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. - Yu Hu 008; Haim Sompolinsky (PLoS Comput. Biol., 2022)

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)

Optimal Quadratic Binding for Relational Reasoning in Vector Symbolic Neural Architectures. - Naoki Hiratani; Haim Sompolinsky (Neural Comput., 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 (CPAL, 2024)

Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers. - Lorenzo Tiberi; Francesca Mignacco; Kazuki Irie; Haim Sompolinsky (NeurIPS, 2024)

Coding schemes in neural networks learning classification tasks. - Alexander van Meegen; Haim Sompolinsky (CoRR, 2024)

Order parameters and phase transitions of continual learning in deep neural networks. - Haozhe Shan; Qianyi Li; Haim Sompolinsky (CoRR, 2024)

Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit. - Zechen Zhang; Haim Sompolinsky (CoRR, 2024)

Diverse capability and scaling of diffusion and auto-regressive models when learning abstract rules. - Binxu Wang; Jiaqi Shang; Haim Sompolinsky (CoRR, 2024)

Simplified derivations for high-dimensional convex learning problems. - David G. Clark; Haim Sompolinsky (CoRR, 2024)

When narrower is better: the narrow width limit of Bayesian parallel branching neural networks. - Zechen Zhang; Haim Sompolinsky (ICLR, 2025)

Unraveling the geometry of visual relational reasoning. - Jiaqi Shang; Gabriel Kreiman; Haim Sompolinsky (CoRR, 2025)

Memorization and Knowledge Injection in Gated LLMs. - Xu Pan; Ely Hahami; Zechen Zhang; Haim Sompolinsky (CoRR, 2025)