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ORCIDHaim Sompolinsky
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2020 – today
- 2024
[i17]Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky:
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers. CoRR abs/2405.15926 (2024)- [i16]Alexander van Meegen, Haim Sompolinsky:
Coding schemes in neural networks learning classification tasks. CoRR abs/2406.16689 (2024) - [i15]Haozhe Shan, Qianyi Li, Haim Sompolinsky:
Order parameters and phase transitions of continual learning in deep neural networks. CoRR abs/2407.10315 (2024) - [i14]Zechen Zhang, Haim Sompolinsky:
Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit. CoRR abs/2407.18807 (2024) - 2023
- [j19]Naoki Hiratani, Haim Sompolinsky:
Optimal Quadratic Binding for Relational Reasoning in Vector Symbolic Neural Architectures. Neural Comput. 35(2): 105-155 (2023) - [i13]Yehonatan Avidan, Qianyi Li, Haim Sompolinsky:
Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime. CoRR abs/2309.04522 (2023) - [i12]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 abs/2312.14285 (2023) - 2022
- [j18]Yu Hu
, Haim Sompolinsky:
The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. PLoS Comput. Biol. 18(7) (2022) - [c16]Qianyi Li, Haim Sompolinsky:
Globally Gated Deep Linear Networks. NeurIPS 2022 - [c15]Weishun Zhong, Ben Sorscher, Daniel Lee, Haim Sompolinsky:
A theory of weight distribution-constrained learning. NeurIPS 2022 - [i11]Uri Cohen, Haim Sompolinsky:
Soft-margin classification of object manifolds. CoRR abs/2203.07040 (2022) - [i10]Naoki Hiratani, Haim Sompolinsky:
Optimal quadratic binding for relational reasoning in vector symbolic neural architectures. CoRR abs/2204.07186 (2022) - [i9]Weishun Zhong, Ben Sorscher, Daniel D. Lee, Haim Sompolinsky:
A theory of learning with constrained weight-distribution. CoRR abs/2206.08933 (2022) - [i8]Qianyi Li, Haim Sompolinsky:
Globally Gated Deep Linear Networks. CoRR abs/2210.17449 (2022) - 2020
- [j17]Madhu S. Advani, Andrew M. Saxe, Haim Sompolinsky:
High-dimensional dynamics of generalization error in neural networks. Neural Networks 132: 428-446 (2020) - [i7]Gadi Naveh, Oded Ben-David, Haim Sompolinsky, Zohar Ringel:
Predicting the outputs of finite networks trained with noisy gradients. CoRR abs/2004.01190 (2020) - [i6]Julia Steinberg, Madhu Advani, Haim Sompolinsky:
A new role for circuit expansion for learning in neural networks. CoRR abs/2008.08653 (2020) - [i5]Qianyi Li, Haim Sompolinsky:
Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Renormalization Group. CoRR abs/2012.04030 (2020)
2010 – 2019
- 2019
- [j16]Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky:
Functional diversity among sensory neurons from efficient coding principles. PLoS Comput. Biol. 15(11) (2019) - 2018
- [j15]SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee:
Learning Data Manifolds with a Cutting Plane Method. Neural Comput. 30(10) (2018) - [j14]Itamar Daniel Landau, Haim Sompolinsky:
Coherent chaos in a recurrent neural network with structured connectivity. PLoS Comput. Biol. 14(12) (2018) - 2017
- [c14]Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky:
Markov Transitions between Attractor States in a Recurrent Neural Network. AAAI Spring Symposia 2017 - [i4]Ran Rubin, L. F. Abbott, Haim Sompolinsky:
Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity. CoRR abs/1705.01502 (2017) - [i3]SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee:
Learning Data Manifolds with a Cutting Plane Method. CoRR abs/1705.09944 (2017) - [i2]SueYeon Chung, Daniel D. Lee, Haim Sompolinsky:
Classification and Geometry of General Perceptual Manifolds. CoRR abs/1710.06487 (2017) - 2016
- [c13]Jonathan Kadmon, Haim Sompolinsky:
Optimal Architectures in a Solvable Model of Deep Networks. NIPS 2016: 4781-4789 - 2015
- [j13]Ariel Furstenberg, Assaf Breska, Haim Sompolinsky, Leon Y. Deouell:
Evidence of Change of Intention in Picking Situations. J. Cogn. Neurosci. 27(11): 2133-2146 (2015) - [i1]SueYeon Chung, Daniel D. Lee, Haim Sompolinsky:
Classification of Manifolds by Single-Layer Neural Networks. CoRR abs/1512.01834 (2015) - 2014
- [r1]Robert Gütig, Haim Sompolinsky:
Tempotron Learning. Encyclopedia of Computational Neuroscience 2014 - 2012
- [j12]Uri Rokni, Haim Sompolinsky:
How the Brain Generates Movement. Neural Comput. 24(2): 289-331 (2012) - 2011
- [c12]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: 39-42 - 2010
- [c11]Surya Ganguli, Haim Sompolinsky:
Short-term memory in neuronal networks through dynamical compressed sensing. NIPS 2010: 667-675 - [c10]Kanaka Rajan, L. F. Abbott, Haim Sompolinsky:
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. NIPS 2010: 1975-1983
2000 – 2009
- 2009
- [j11]Yoram Burak, Sam Lewallen, Haim Sompolinsky:
Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons. Neural Comput. 21(8): 2269-2308 (2009) - 2006
- [j10]Maoz Shamir, Haim Sompolinsky:
Implications of Neuronal Diversity on Population Coding. Neural Comput. 18(8): 1951-1986 (2006) - 2004
- [j9]Maoz Shamir, Haim Sompolinsky:
Nonlinear Population Codes. Neural Comput. 16(6): 1105-1136 (2004) - 2003
- [j8]Oren Shriki, David Hansel, Haim Sompolinsky:
Rate Models for Conductance-Based Cortical Neuronal Networks. Neural Comput. 15(8): 1809-1841 (2003) - 2001
- [c9]Maoz Shamir, Haim Sompolinsky:
Correlation Codes in Neuronal Populations. NIPS 2001: 277-284 - 2000
- [c8]Oren Shriki, Haim Sompolinsky, Daniel D. Lee:
An Information Maximization Approach to Overcomplete and Recurrent Representations. NIPS 2000: 612-618
1990 – 1999
- 1999
- [c7]Daniel D. Lee, Uri Rokni, Haim Sompolinsky:
Algorithms for Independent Components Analysis and Higher Order Statistics. NIPS 1999: 491-497 - 1998
- [j7]Carl van Vreeswijk, Haim Sompolinsky:
Chaotic Balanced State in a Model Of Cortical Circuits. Neural Comput. 10(6): 1321-1371 (1998) - [c6]Hyoungsoo Yoon, Haim Sompolinsky:
The Effect of Correlations on the Fisher Information of Population Codes. NIPS 1998: 167-173 - [c5]Daniel D. Lee, Haim Sompolinsky:
Learning a Continuous Hidden Variable Model for Binary Data. NIPS 1998: 515-521 - 1997
- [j6]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): 57-77 (1997) - 1996
- [j5]David Hansel, Haim Sompolinsky:
Chaos and synchrony in a model of a hypercolumn in visual cortex. J. Comput. Neurosci. 3(1): 7-34 (1996) - [j4]Germán Mato, Haim Sompolinsky:
Neural network models of perceptual learning of angle discrimination. Neural Comput. 8(2): 270-299 (1996) - 1994
- [j3]Haim Sompolinsky, Michail Tsodyks:
Segmentation by a Network of Oscillators with Stored Memories. Neural Comput. 6(4): 642-657 (1994) - [c4]N. Barkai, H. Sebastian Seung, Haim Sompolinsky:
On-line Learning of Dichotomies. NIPS 1994: 303-310 - 1993
- [j2]E. R. Grannan, D. Kleinfeld, Haim Sompolinsky:
Stimulus-Dependent Synchronization of Neuronal Assemblies. Neural Comput. 5(4): 550-569 (1993) - [c3]Iris Ginzburg, Haim Sompolinsky:
Correlation Functions in a Large Stochastic Network. NIPS 1993: 471-476 - 1992
- [j1]Haim Sompolinsky, Michail Tsodyks:
Processing of Sensory Information by a Network of Oscillators with Memory. Int. J. Neural Syst. 3(Supplement): 51-56 (1992) - [c2]H. Sebastian Seung, Manfred Opper, Haim Sompolinsky:
Query by Committee. COLT 1992: 287-294 - 1991
- [c1]H. Sebastian Seung, Haim Sompolinsky, Naftali Tishby:
Learning Curves in Large Neural Networks. COLT 1991: 112-127
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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