Author Info
Name
Affiliation
Papers
TIMOTHÉE MASQUELIER
Centre de Recherche Cerveau et Cognition, Universit¿¿ Toulouse 3, Centre National de la Recherche Scientifique, Facult¿¿ de M¿¿decine de Rangueil, Toulouse 31062, France, and Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona E-08003, Spain timothee.masquelier@alum.mit.edu|c|
23
Collaborators
Citations 
PageRank 
22
388
19.28
Referers 
Referees 
References 
875
657
372
Search Limit
100875
Title
Citations
PageRank
Year
Spiking Neural Networks Trained via Proxy00.342022
FAST THRESHOLD OPTIMIZATION FOR MULTI-LABEL AUDIO TAGGING USING SURROGATE GRADIENT LEARNING00.342021
Temporal Backpropagation For Spiking Neural Networks With One Spike Per Neuron40.392020
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks50.432019
Deep Learning in Spiking Neural Networks.441.312019
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron.30.412019
Representation Learning using Event-based STDP.40.412018
First-Spike-Based Visual Categorization Using Reward-Modulated STDP.150.582018
Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing.00.342018
Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition.60.492018
STDP-based spiking deep convolutional neural networks for object recognition.481.262018
Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection.20.362018
Live Demonstration: Hardware Implementation Of Convolutional Stdp For On-Line Visual Feature Learning00.342017
Acquisition of visual features through probabilistic spike-timing-dependent plasticity50.472016
STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons.00.342016
STDP-based spiking deep neural networks for object recognition.00.342016
Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.70.472016
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition.241.022015
Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model.40.432012
STDP allows fast rate-modulated coding with Poisson-like spike trains.110.812011
Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity110.672010
Competitive STDP-based spike pattern learning.722.752009
Unsupervised Learning Of Visual Features Through Spike Timing Dependent Plasticity1235.012007