Joel Zylberberg 个人学术档案

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	总计	2019 年至今
引用	2893	1958
h 指数	23	18
i10 指数	28	24

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Eric Shea-BrownApplied Mathematics, University of Washington在 uw.edu 的电子邮件经过验证
Michael R DeWeeseProfessor of Physics and Neuroscience, University of California at Berkeley在 berkeley.edu 的电子邮件经过验证
Levon PogosianProfessor of Physics, Simon Fraser University在 sfu.ca 的电子邮件经过验证
Alessandra SilvestriLeiden University在 lorentz.leidenuniv.nl 的电子邮件经过验证
Zuo Guang YeChemistry, Simon Fraser University在 sfu.ca 的电子邮件经过验证
Fred RiekeProfessor, Physiology and Biophysics and HHMI, University of Washington在 uw.edu 的电子邮件经过验证
Elijah ChristensenUniversity of Colorado在 cuanschutz.edu 的电子邮件经过验证
Alona FysheAssociate Professor, Dept. of Computing Science & Dept. of Psychology, University of Alberta在 ualberta.ca 的电子邮件经过验证
Maxwell H. TurnerSUNY Albany在 albany.edu 的电子邮件经过验证
Greg FieldAssociate Professor of Ophthalmology, UCLA在 ucla.edu 的电子邮件经过验证
Alex Cayco-GajicÉcole Normale Supérieure Paris在 ens.fr 的电子邮件经过验证
Callie FedererRadiaSoft在 radiasoft.net 的电子邮件经过验证
Paul D. KingVisiting Scholar in Computational Neuroscience, University of California at Berkeley在 pking.org 的电子邮件经过验证
William F KindelUniversity of Colorado School of Medicine在 ucdenver.edu 的电子邮件经过验证
Timothy P. LillicrapDirector of Research, Google DeepMind在 google.com 的电子邮件经过验证
Colleen J. GillonPostdoc, Bioengineering, Imperial College London在 imperial.ac.uk 的电子邮件经过验证
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFAR在 umontreal.ca 的电子邮件经过验证
Blake RichardsMila + McGill University在 mila.quebec 的电子邮件经过验证
Jérôme LecoqAssociate Investigator, Allen Institute for Brain Science在 alleninstitute.org 的电子邮件经过验证
Jason E PinaPhysics & Astronomy + Centre for Vision Research, York University在 yorku.ca 的电子邮件经过验证

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Joel Zylberberg

York University and CIFAR

在 yorku.ca 的电子邮件经过验证 - 首页

Computational Neuroscience Vision Machine Learning Artificial Intelligence


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019	851	2019
Searching for modified growth patterns with tomographic surveys GB Zhao, L Pogosian, A Silvestri, J Zylberberg Physical Review D—Particles, Fields, Gravitation, and Cosmology 79 (8), 083513, 2009	327	2009
A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields J Zylberberg, JT Murphy, MR DeWeese PLoS computational biology 7 (10), e1002250, 2011	241	2011
Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory J Zylberberg, BW Strowbridge Annual review of neuroscience 40 (1), 603-627, 2017	181	2017
Bismuth Aluminate: A New High-T_C Lead-Free Piezo-/ferroelectric J Zylberberg, A A. Belik, E Takayama-Muromachi, ZG Ye Chemistry of Materials 19 (26), 6385-6390, 2007	159	2007
Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1 PD King, J Zylberberg, MR DeWeese Journal of Neuroscience 33 (13), 5475-5485, 2013	151	2013
Direction-selective circuits shape noise to ensure a precise population code J Zylberberg, J Cafaro, MH Turner, E Shea-Brown, F Rieke Neuron 89 (2), 369-383, 2016	144	2016
Cosmological tests of general relativity with future tomographic surveys GB Zhao, L Pogosian, A Silvestri, J Zylberberg Physical Review Letters 103 (24), 241301, 2009	126	2009
Using deep learning to probe the neural code for images in primary visual cortex WF Kindel, ED Christensen, J Zylberberg Journal of vision 19 (4), 29-29, 2019	83*	2019
The sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes Y Hu, J Zylberberg, E Shea-Brown PLoS computational biology 10 (2), e1003469, 2014	74	2014
Robust information propagation through noisy neural circuits J Zylberberg, A Pouget, PE Latham, E Shea-Brown PLoS computational biology 13 (4), e1005497, 2017	57	2017
The role of untuned neurons in sensory information coding J Zylberberg BioRxiv, 134379, 2017	39	2017
Improved object recognition using neural networks trained to mimic the brain’s statistical properties C Federer, H Xu, A Fyshe, J Zylberberg Neural Networks 131, 103-114, 2020	38	2020
The language of the brain: real-world neural population codes JA Pruszynski, J Zylberberg Current opinion in neurobiology 58, 30-36, 2019	36	2019
Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images J Zylberberg, MR DeWeese PLoS computational biology 9 (8), e1003182, 2013	35	2013
Improved dielectric properties of bismuth-doped LaAlO3 J Zylberberg, ZG Ye Journal of applied physics 100 (8), 2006	34	2006
Triplet correlations among similarly tuned cells impact population coding NA Cayco-Gajic, J Zylberberg, E Shea-Brown Frontiers in computational neuroscience 9, 57, 2015	33	2015
Improvements of the DRAGON recoil separator at ISAC C Vockenhuber, L Buchmann, J Caggiano, AA Chen, JM D’Auria, ... Nuclear Instruments and Methods in Physics Research Section B: Beam …, 2008	32	2008
Learning from unexpected events in the neocortical microcircuit CJ Gillon, JE Pina, JA Lecoq, R Ahmed, YN Billeh, S Caldejon, ... BioRxiv, 2021.01. 15.426915, 2021	29	2021
Temperature dependence of the capacitance of a ferroelectric material J Bechhoefer, Y Deng, J Zylberberg, C Lei, ZG Ye American Journal of Physics 75 (11), 1046-1053, 2007	26	2007

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