Haim Sompolinsky

Publication Activity (10 Years)

Years Active: 1991-2024
Publications (10 Years): 26

Top Topics

Statistical Mechanics

Generalization Error

Covariance Matrices

Top Venues

PLoS Comput. Biol.

Publications

Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers. CoRR (2024)
Zechen Zhang, Haim Sompolinsky
Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit. CoRR (2024)
Haozhe Shan, Qianyi Li, Haim Sompolinsky
Order parameters and phase transitions of continual learning in deep neural networks. CoRR (2024)
Alexander van Meegen, Haim Sompolinsky
Coding schemes in neural networks learning classification tasks. CoRR (2024)
Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. CoRR (2023)
Naoki Hiratani, Haim Sompolinsky
Optimal Quadratic Binding for Relational Reasoning in Vector Symbolic Neural Architectures. Neural Comput. 35 (2) (2023)
Yehonatan Avidan, Qianyi Li, Haim Sompolinsky
Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime. CoRR (2023)
Weishun Zhong, Ben Sorscher, Daniel D. Lee, Haim Sompolinsky
A theory of learning with constrained weight-distribution. CoRR (2022)
Yu Hu, Haim Sompolinsky
The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. PLoS Comput. Biol. 18 (7) (2022)
Qianyi Li, Haim Sompolinsky
Globally Gated Deep Linear Networks. CoRR (2022)
Qianyi Li, Haim Sompolinsky
Globally Gated Deep Linear Networks. NeurIPS (2022)
Weishun Zhong, Ben Sorscher, Daniel Lee, Haim Sompolinsky
A theory of weight distribution-constrained learning. NeurIPS (2022)
Naoki Hiratani, Haim Sompolinsky
Optimal quadratic binding for relational reasoning in vector symbolic neural architectures. CoRR (2022)
Uri Cohen, Haim Sompolinsky
Soft-margin classification of object manifolds. CoRR (2022)
Julia Steinberg, Madhu Advani, Haim Sompolinsky
A new role for circuit expansion for learning in neural networks. CoRR (2020)
Gadi Naveh, Oded Ben-David, Haim Sompolinsky, Zohar Ringel
Predicting the outputs of finite networks trained with noisy gradients. CoRR (2020)
Madhu S. Advani, Andrew M. Saxe, Haim Sompolinsky
High-dimensional dynamics of generalization error in neural networks. Neural Networks 132 (2020)
Qianyi Li, Haim Sompolinsky
Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Renormalization Group. CoRR (2020)
Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky
Functional diversity among sensory neurons from efficient coding principles. PLoS Comput. Biol. 15 (11) (2019)
SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee
Learning Data Manifolds with a Cutting Plane Method. Neural Comput. 30 (10) (2018)
Itamar Daniel Landau, Haim Sompolinsky
Coherent chaos in a recurrent neural network with structured connectivity. PLoS Comput. Biol. 14 (12) (2018)
Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky
Markov Transitions between Attractor States in a Recurrent Neural Network. AAAI Spring Symposia (2017)
SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee
Learning Data Manifolds with a Cutting Plane Method. CoRR (2017)
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Classification and Geometry of General Perceptual Manifolds. CoRR (2017)
Ran Rubin, L. F. Abbott, Haim Sompolinsky
Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity. CoRR (2017)
Jonathan Kadmon, Haim Sompolinsky
Optimal Architectures in a Solvable Model of Deep Networks. NIPS (2016)
Ariel Furstenberg, Assaf Breska, Haim Sompolinsky, Leon Y. Deouell
Evidence of Change of Intention in Picking Situations. J. Cogn. Neurosci. 27 (11) (2015)
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Classification of Manifolds by Single-Layer Neural Networks. CoRR (2015)
Uri Rokni, Haim Sompolinsky
How the Brain Generates Movement. Neural Comput. 24 (2) (2012)
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
Introducing the Human Brain Project. FET (2011)
Surya Ganguli, Haim Sompolinsky
Short-term memory in neuronal networks through dynamical compressed sensing. NIPS (2010)
Kanaka Rajan, L. F. Abbott, Haim Sompolinsky
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. NIPS (2010)
Yoram Burak, Sam Lewallen, Haim Sompolinsky
Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons. Neural Comput. 21 (8) (2009)
Maoz Shamir, Haim Sompolinsky
Implications of Neuronal Diversity on Population Coding. Neural Comput. 18 (8) (2006)
Maoz Shamir, Haim Sompolinsky
Nonlinear Population Codes. Neural Comput. 16 (6) (2004)
Oren Shriki, David Hansel, Haim Sompolinsky
Rate Models for Conductance-Based Cortical Neuronal Networks. Neural Comput. 15 (8) (2003)
Maoz Shamir, Haim Sompolinsky
Correlation Codes in Neuronal Populations. NIPS (2001)
Oren Shriki, Haim Sompolinsky, Daniel D. Lee
An Information Maximization Approach to Overcomplete and Recurrent Representations. NIPS (2000)
Daniel D. Lee, Uri Rokni, Haim Sompolinsky
Algorithms for Independent Components Analysis and Higher Order Statistics. NIPS (1999)
Daniel D. Lee, Haim Sompolinsky
Learning a Continuous Hidden Variable Model for Binary Data. NIPS (1998)
Carl van Vreeswijk, Haim Sompolinsky
Chaotic Balanced State in a Model Of Cortical Circuits. Neural Comput. 10 (6) (1998)
Hyoungsoo Yoon, Haim Sompolinsky
The Effect of Correlations on the Fisher Information of Population Codes. NIPS (1998)
Rani Ben-Yishai, David Hansel, Haim Sompolinsky
Traveling Waves and the Processing of Weakly Tuned Inputs in a Cortical Network Module. J. Comput. Neurosci. 4 (1) (1997)
David Hansel, Haim Sompolinsky
Chaos and synchrony in a model of a hypercolumn in visual cortex. J. Comput. Neurosci. 3 (1) (1996)
Germán Mato, Haim Sompolinsky
Neural network models of perceptual learning of angle discrimination. Neural Comput. 8 (2) (1996)
Haim Sompolinsky, Michail Tsodyks
Segmentation by a Network of Oscillators with Stored Memories. Neural Comput. 6 (4) (1994)
N. Barkai, H. Sebastian Seung, Haim Sompolinsky
On-line Learning of Dichotomies. NIPS (1994)
E. R. Grannan, D. Kleinfeld, Haim Sompolinsky
Stimulus-Dependent Synchronization of Neuronal Assemblies. Neural Comput. 5 (4) (1993)
Iris Ginzburg, Haim Sompolinsky
Correlation Functions in a Large Stochastic Network. NIPS (1993)
H. Sebastian Seung, Manfred Opper, Haim Sompolinsky
Query by Committee. COLT (1992)
Haim Sompolinsky, Michail Tsodyks
Processing of Sensory Information by a Network of Oscillators with Memory. Int. J. Neural Syst. 3 (Supplement) (1992)
H. Sebastian Seung, Haim Sompolinsky, Naftali Tishby
Learning Curves in Large Neural Networks. COLT (1991)