Timothée Masquelier

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Simon J. ThorpeCNRS Research Director (DRCE2), CerCo (CNRS-UT3), TMBI (Univ. Toulouse), FranceVerified email at cerco.ups-tlse.fr
Saeed Reza KheradpishehShahid Beheshti UniversityVerified email at sbu.ac.ir
Mohammad GANJTABESHProfessor of Computer Science (University of Tehran)Verified email at ut.ac.ir
Bernabe Linares-BarrancoInstituto de Microelectrónica de Sevilla, IMSE-CNM, Universidad de Sevilla, CSICVerified email at imse-cnm.csic.es
serrano-gotarredonaInstituto de Microlectronica de Sevilla, CSIC, Universidad de SevillaVerified email at imse-cnm.csic.es
Zhaofei YuPeking UniversityVerified email at pku.edu.cn
Yonghong Tian, Professor, IEEE FellowSchool of CS and School of ECE at Peking University, Pengcheng LaboratoryVerified email at pku.edu.cn
Milad MozafariPostdoc, CNRS, CerCoVerified email at cnrs.fr
Abbas Nowzari-Daliniassociate prof., university of tehranVerified email at ut.ac.ir
GUSTAVO DECOUniversity Pompeu Fabra - Center for Brain and Cognition - ICREAVerified email at upf.edu
Amir TavanaeiAmazonVerified email at amazon.com
Anthony S. MaidaSchool of Computing and Informatics, UL LafayetteVerified email at louisiana.edu
Tiejun HuangProfessor,School of EE&CS, Peking UniversityVerified email at pku.edu.cn
Wei Fang (方维)Peking UniversityVerified email at pku.edu.cn
Thomas PellegriniLecturer, University of Toulouse, IRITVerified email at irit.fr
Giacomo IndiveriInstitute of Neuroinformatics, University of Zurich and ETH ZurichVerified email at ini.uzh.ch
Themis ProdromakisRegius Chair of Engineering, Centre for Electronics Frontiers, University of EdinburghVerified email at ed.ac.uk
Yanqi Chen 陈彦骐Ph.D. student, School of Computer Science, Peking UniversityVerified email at pku.edu.cn
maryam mirsadeghiResearcherVerified email at aut.ac.ir
Pierre KornprobstResearcher, INRIA, FranceVerified email at inria.fr

Timothée Masquelier

CNRS Researcher (DR2), Cerco (CNRS-UT3), TMBI (Univ. Toulouse)

Verified email at cnrs.fr - Homepage

AI computational neuroscience deep learning spiking neural networks vision


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Deep learning in spiking neural networks A Tavanaei, M Ghodrati, SR Kheradpisheh, T Masquelier, A Maida Neural Networks 111, 47-63, 2019	917	2019
STDP-based spiking deep convolutional neural networks for object recognition SR Kheradpisheh, M Ganjtabesh, SJ Thorpe, T Masquelier Neural Networks, 2017	724	2017
Unsupervised learning of visual features through spike timing dependent plasticity T Masquelier, SJ Thorpe PLoS Computational Biology 3 (2), e31, 2007	620	2007
On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex C Zamarreño-Ramos, LA Camuñas-Mesa, JA Pérez-Carrasco, ... Frontiers in neuroscience 5, 2011	572	2011
STDP and STDP variations with memristors for spiking neuromorphic learning systems T Serrano-Gotarredona, T Masquelier, T Prodromakis, G Indiveri, ... Frontiers in Neuroscience 7, 2013	512	2013
Competitive STDP-based spike pattern learning T Masquelier, R Guyonneau, SJ Thorpe Neural computation 21 (5), 1259-1276, 2009	368	2009
Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains T Masquelier, R Guyonneau, SJ Thorpe PloS one 3 (1), e1377, 2008	350	2008
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks W Fang, Z Yu, Y Chen, T Masquelier, T Huang, Y Tian arXiv preprint arXiv:2007.05785, 2020	281	2020
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks M Mozafari, M Ganjtabesh, A Nowzari-Dalini, SJ Thorpe, T Masquelier Pattern Recognition 94, 87-95, 2019	198*	2019
Deep residual learning in spiking neural networks W Fang, Z Yu, Y Chen, T Huang, T Masquelier, Y Tian Thirty-Fifth Conference on Neural Information Processing Systems, 2021	193	2021
Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme T Masquelier, E Hugues, G Deco, SJ Thorpe The Journal of Neuroscience 29 (43), 13484-13493, 2009	185	2009
Temporal backpropagation for spiking neural networks with one spike per neuron SR Kheradpisheh, T Masquelier International Journal of Neural Systems, 2020	183	2020
Deep networks can resemble human feed-forward vision in invariant object recognition SR Kheradpisheh, M Ghodrati, M Ganjtabesh, T Masquelier Scientific reports 6 (1), 32672, 2016	180	2016
First-spike-based visual categorization using reward-modulated STDP M Mozafari, SR Kheradpisheh, T Masquelier, A Nowzari-Dalini, ... IEEE transactions on neural networks and learning systems 29 (12), 6178-6190, 2018	151	2018
Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition SR Kheradpisheh, M Ganjtabesh, T Masquelier Neurocomputing 205, 382-392, 2016	134	2016
Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model T Masquelier Journal of computational neuroscience 32 (3), 425-441, 2012	130	2012
Spyketorch: Efficient simulation of convolutional spiking neural networks with at most one spike per neuron M Mozafari, M Ganjtabesh, A Nowzari-Dalini, T Masquelier Frontiers in neuroscience, 625, 2019	112	2019
Neural variability, or lack thereof T Masquelier Frontiers in computational neuroscience 7, 7, 2013	70	2013
Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity T Masquelier, SJ Thorpe The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010	63	2010
The role of rhythmic neural synchronization in rest and task conditions G Deco, A Buehlmann, T Masquelier, E Hugues Frontiers in human neuroscience 5, 2011	54	2011

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