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Haim Sompolinsky

Showing results (21-30 of 66) with videos related to

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Nature Communications|April 9, 2025
Coding schemes in neural networks learning classification tasksAlexander van Meegen, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|October 17, 2022
Neural representational geometry underlies few-shot concept learningBen Sorscher, Surya Ganguli, Haim Sompolinsky
Physical Review Letters|January 15, 2011
Theory of spike timing-based neural classifiersRan Rubin, Rémi Monasson, Haim Sompolinsky
Plos Biology|December 29, 2007
A neural computation for visual acuity in the presence of eye movementsXaq Pitkow, Haim Sompolinsky, Markus Meister
Arxiv|March 10, 2025
Unraveling the Geometry of Visual Relational ReasoningJiaqi Shang, Gabriel Kreiman, Haim Sompolinsky
Neural Computation|May 5, 2009
Stimulus-dependent correlations in threshold-crossing spiking neuronsYoram Burak, Sam Lewallen, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|June 24, 2024
Representations and generalization in artificial and brain neural networksQianyi Li, Ben Sorscher, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|February 6, 2026
Order parameters and phase transitions of continual learning in deep neural networksHaozhe Shan, Qianyi Li, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|November 21, 2008
Memory traces in dynamical systemsSurya Ganguli, Dongsung Huh, Haim Sompolinsky
Neural Computation|September 27, 2003
Rate models for conductance-based cortical neuronal networksOren Shriki, David Hansel, Haim Sompolinsky
Pageof 7

Showing results (21-30 of 66) with videos related to

Sort By:
Pageof 7
Nature Communications|April 9, 2025
Coding schemes in neural networks learning classification tasksAlexander van Meegen, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|October 17, 2022
Neural representational geometry underlies few-shot concept learningBen Sorscher, Surya Ganguli, Haim Sompolinsky
Physical Review Letters|January 15, 2011
Theory of spike timing-based neural classifiersRan Rubin, Rémi Monasson, Haim Sompolinsky
Plos Biology|December 29, 2007
A neural computation for visual acuity in the presence of eye movementsXaq Pitkow, Haim Sompolinsky, Markus Meister
Arxiv|March 10, 2025
Unraveling the Geometry of Visual Relational ReasoningJiaqi Shang, Gabriel Kreiman, Haim Sompolinsky
Neural Computation|May 5, 2009
Stimulus-dependent correlations in threshold-crossing spiking neuronsYoram Burak, Sam Lewallen, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|June 24, 2024
Representations and generalization in artificial and brain neural networksQianyi Li, Ben Sorscher, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|February 6, 2026
Order parameters and phase transitions of continual learning in deep neural networksHaozhe Shan, Qianyi Li, Haim Sompolinsky
Proceedings of the National Academy of Sciences of the United States of America|November 21, 2008
Memory traces in dynamical systemsSurya Ganguli, Dongsung Huh, Haim Sompolinsky
Neural Computation|September 27, 2003
Rate models for conductance-based cortical neuronal networksOren Shriki, David Hansel, Haim Sompolinsky
Pageof 7