Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. - Timothée Masquelier; Simon J. Thorpe (PLoS Comput. Biol., 2007)

Competitive STDP-Based Spike Pattern Learning. - Timothée Masquelier; Rudy Guyonneau; Simon J. Thorpe (Neural Comput., 2009)

Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity. - Timothée Masquelier; Simon J. Thorpe (IJCNN, 2010)

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains. - Matthieu Gilson; Timothée Masquelier; Etienne Hugues (PLoS Comput. Biol., 2011)

Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model. - Timothée Masquelier (J. Comput. Neurosci., 2012)

Neural variability, or lack thereof. - Timothée Masquelier (Frontiers Comput. Neurosci., 2013)

Deep Networks Resemble Human Feed-forward Vision in Invariant Object Recognition. - Saeed Reza Kheradpisheh; Masoud Ghodrati; Mohammad Ganjtabesh; Timothée Masquelier (CoRR, 2015)

Acquisition of visual features through probabilistic spike-timing-dependent plasticity. - Amirhossein Tavanaei; Timothée Masquelier; Anthony S. Maida (IJCNN, 2016)

STDP-based spiking deep neural networks for object recognition. - Saeed Reza Kheradpisheh; Mohammad Ganjtabesh; Simon J. Thorpe; Timothée Masquelier (CoRR, 2016)

STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons. - Timothée Masquelier (CoRR, 2016)

Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder. - Saeed Reza Kheradpisheh; Masoud Ghodrati; Mohammad Ganjtabesh; Timothée Masquelier (Frontiers Comput. Neurosci., 2016)

Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition. - Saeed Reza Kheradpisheh; Mohammad Ganjtabesh; Timothée Masquelier (Neurocomputing, 2016)

Live demonstration: Hardware implementation of convolutional STDP for on-line visual feature learning. - Amirreza Yousefzadeh; Timothée Masquelier; Teresa Serrano-Gotarredona; Bernabé Linares-Barranco (ISCAS, 2017)

Hardware implementation of convolutional STDP for on-line visual feature learning. - Amirreza Yousefzadeh; Timothée Masquelier; Teresa Serrano-Gotarredona; Bernabé Linares-Barranco (ISCAS, 2017)

Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition. - Milad Mozafari; Mohammad Ganjtabesh; Abbas Nowzari-Dalini; Simon J. Thorpe; Timothée Masquelier (CoRR, 2018)

Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection. - Timothée Masquelier; Saeed Reza Kheradpisheh (Frontiers Comput. Neurosci., 2018)

Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing. - Jacob Huth; Timothée Masquelier; Angelo Arleo (Frontiers Neuroinformatics, 2018)

STDP-based spiking deep convolutional neural networks for object recognition. - Saeed Reza Kheradpisheh; Mohammad Ganjtabesh; Simon J. Thorpe; Timothée Masquelier (Neural Networks, 2018)

Representation learning using event-based STDP. - Amirhossein Tavanaei; Timothée Masquelier; Anthony S. Maida (Neural Networks, 2018)

First-Spike-Based Visual Categorization Using Reward-Modulated STDP. - Milad Mozafari; Saeed Reza Kheradpisheh; Timothée Masquelier; Abbas Nowzari-Dalini; Mohammad Ganjtabesh (IEEE Trans. Neural Networks Learn. Syst., 2018)

Spike-Timing-Dependent-Plasticity with Memristors. - Teresa Serrano-Gotarredona; Timothée Masquelier; Bernabé Linares-Barranco (Handbook of Memristor Networks, 2019)

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron. - Milad Mozafari; Mohammad Ganjtabesh; Abbas Nowzari-Dalini; Timothée Masquelier (CoRR, 2019)

S4NN: temporal backpropagation for spiking neural networks with one spike per neuron. - Saeed Reza Kheradpisheh; Timothée Masquelier (CoRR, 2019)

Technical report: supervised training of convolutional spiking neural networks with PyTorch. - Romain Zimmer; Thomas Pellegrini; Srisht Fateh Singh; Timothée Masquelier (CoRR, 2019)

Deep learning in spiking neural networks. - Amirhossein Tavanaei; Masoud Ghodrati; Saeed Reza Kheradpisheh; Timothée Masquelier; Anthony Maida (Neural Networks, 2019)

Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks. - Milad Mozafari; Mohammad Ganjtabesh; Abbas Nowzari-Dalini; Simon J. Thorpe; Timothée Masquelier (Pattern Recognit., 2019)

Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network. - Pouya Soltani Zarrin; Romain Zimmer; Christian Wenger; Timothée Masquelier (IWBBIO, 2020)

Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron. - Saeed Reza Kheradpisheh; Timothée Masquelier (Int. J. Neural Syst., 2020)

Fast Threshold Optimization for Multi-Label Audio Tagging Using Surrogate Gradient Learning. - Thomas Pellegrini; Timothée Masquelier (ICASSP, 2021)

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks. - Wei Fang 006; Zhaofei Yu; Yanqi Chen; Timothée Masquelier; Tiejun Huang 001; Yonghong Tian 001 (ICCV, 2021)

Deep Residual Learning in Spiking Neural Networks. - Wei Fang 006; Zhaofei Yu; Yanqi Chen; Tiejun Huang 001; Timothée Masquelier; Yonghong Tian 001 (NeurIPS, 2021)

Low-Activity Supervised Convolutional Spiking Neural Networks Applied to Speech Commands Recognition. - Thomas Pellegrini; Romain Zimmer; Timothée Masquelier (SLT, 2021)

Spike-based Residual Blocks. - Wei Fang 006; Zhaofei Yu; Timothée Masquelier; Yanqi Chen; Tiejun Huang 001; Yonghong Tian 001 (CoRR, 2021)

Back-propagation Now Works in Spiking Neural Networks!. - Timothée Masquelier (ERCIM News, 2021)

Event-Based Trajectory Prediction Using Spiking Neural Networks. - Guillaume Debat; Tushar Chauhan; Benoit Cottereau; Timothée Masquelier; Michel Paindavoine; Robin Baures (Frontiers Comput. Neurosci., 2021)

STiDi-BP: Spike time displacement based error backpropagation in multilayer spiking neural networks. - Maryam Mirsadeghi; Majid Shalchian; Saeed Reza Kheradpisheh; Timothée Masquelier (Neurocomputing, 2021)

Training Spiking Neural Networks with Event-driven Backpropagation. - Yaoyu Zhu; Zhaofei Yu; Wei Fang 006; Xiaodong Xie; Tiejun Huang 001; Timothée Masquelier (NeurIPS, 2022)

Spiking Neural Networks Trained via Proxy. - Saeed Reza Kheradpisheh; Maryam Mirsadeghi; Timothée Masquelier (IEEE Access, 2022)

StereoSpike: Depth Learning With a Spiking Neural Network. - Ulysse Rançon; Javier Cuadrado-Anibarro; Benoit R. Cottereau; Timothée Masquelier (IEEE Access, 2022)

Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing. - Alireza Azadbakht; Saeed Reza Kheradpisheh; Ismail Khalfaoui Hassani; Timothée Masquelier (CoRR, 2022)

Encrypted internet traffic classification using a supervised spiking neural network. - Ali Rasteh; Florian Delpech; Carlos Aguilar Melchor; Romain Zimmer; Saeed Bagheri Shouraki; Timothée Masquelier (Neurocomputing, 2022)

BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning. - Saeed Reza Kheradpisheh; Maryam Mirsadeghi; Timothée Masquelier (Neural Process. Lett., 2022)

Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation. - Ilyass Hammouamri; Timothée Masquelier; Dennis George Wilson (Trans. Mach. Learn. Res., 2022)

Dilated convolution with learnable spacings. - Ismail Khalfaoui Hassani; Thomas Pellegrini; Timothée Masquelier (ICLR, 2023)

Adapting a ConvNeXt Model to Audio Classification on AudioSet. - Thomas Pellegrini; Ismail Khalfaoui Hassani; Etienne Labbé; Timothée Masquelier (INTERSPEECH, 2023)

Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies. - Wei Fang 006; Zhaofei Yu; Zhaokun Zhou; Ding Chen; Yanqi Chen; Zhengyu Ma; Timothée Masquelier; Yonghong Tian 001 (NeurIPS, 2023)

Optical Flow estimation with Event-based Cameras and Spiking Neural Networks. - Javier Cuadrado; Ulysse Rançon; Benoit Cottereau; Francisco Barranco; Timothée Masquelier (CoRR, 2023)

Parallel Spiking Neurons with High Efficiency and Long-term Dependencies Learning Ability. - Wei Fang 006; Zhaofei Yu; Zhaokun Zhou; Yanqi Chen; Zhengyu Ma; Timothée Masquelier; Yonghong Tian 001 (CoRR, 2023)

Dilated Convolution with Learnable Spacings: beyond bilinear interpolation. - Ismail Khalfaoui Hassani; Thomas Pellegrini; Timothée Masquelier (CoRR, 2023)

Audio classification with Dilated Convolution with Learnable Spacings. - Ismail Khalfaoui Hassani; Timothée Masquelier; Thomas Pellegrini (CoRR, 2023)

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. - Wei Fang 006; Yanqi Chen; Jianhao Ding; Zhaofei Yu; Timothée Masquelier; Ding Chen; Liwei Huang; Huihui Zhou; Guoqi Li; Yonghong Tian 001 (CoRR, 2023)

Spike time displacement-based error backpropagation in convolutional spiking neural networks. - Maryam Mirsadeghi; Majid Shalchian; Saeed Reza Kheradpisheh; Timothée Masquelier (Neural Comput. Appl., 2023)

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification. - Zahra Imani; Mehdi Ezoji; Timothée Masquelier (J. Comput. Neurosci., 2023)

Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings. - Ilyass Hammouamri; Ismail Khalfaoui Hassani; Timothée Masquelier (ICLR, 2024)

Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning. - Alireza Nadafian; Milad Mozafari; Timothée Masquelier; Mohammad Ganjtabesh (CoRR, 2024)

Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study. - Rabih Chamas; Ismail Khalfaoui Hassani; Timothée Masquelier (CoRR, 2024)

ETTFS: An Efficient Training Framework for Time-to-First-Spike Neuron. - Kaiwei Che; Wei Fang 006; Zhengyu Ma; Li Yuan 007; Timothée Masquelier; Yonghong Tian 001 (CoRR, 2024)

A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses. - Ulysse Rançon; Timothée Masquelier; Benoit R. Cottereau (PLoS Comput. Biol., 2024)

A 23.5 TOPS/W Depthwise Separable Convolution Accelerator for Event-based Depth Estimation. - Andres Brito; Tomomasa Yamasaki; Ulysse Rançon; Timothée Masquelier; Benoit R. Cottereau; Anh Tuan Do; Bo Wang 020 (ISCAS, 2025)

Channel-wise Parallelizable Spiking Neuron with Multiplication-free Dynamics and Large Temporal Receptive Fields. - Peng Xue; Wei Fang 006; Zhengyu Ma; Zihan Huang; Zhaokun Zhou; Yonghong Tian 001; Timothée Masquelier; Huihui Zhou (CoRR, 2025)