T. Masquelier

发表

Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

S. Thorpe, R. Guyonneau, T. Masquelier, 2008, PloS one.

Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

Timothée Masquelier, Simon J. Thorpe, S. Thorpe, 2007, PLoS Comput. Biol..

Technical report: supervised training of convolutional spiking neural networks with PyTorch pdf

Thomas Pellegrini, Srisht Fateh Singh, Timoth'ee Masquelier, 2019, ArXiv.

STDP-based spiking deep convolutional neural networks for object recognition

S. Thorpe, T. Masquelier, S. R. Kheradpisheh, 2018 .

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

Zhaofei Yu, Yonghong Tian, Tiejun Huang, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

STDP-based spiking deep neural networks for object recognition

Timothée Masquelier, Simon J. Thorpe, Mohammad Ganjtabesh, 2016, Neural Networks.

Sub-Optimality of the Early Visual System Explained Through Biologically Plausible Plasticity

T. Masquelier, B. Cottereau, Tushar Chauhan, 2019, bioRxiv.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Minoru Asada, Anders M. Dale, Anna Devor, 2017, BMC Neuroscience.

The wave of first spikes provides robust spatial cues for retinal information processing

P. Kornprobst, T. Masquelier, E. Sernagor, 2014 .

The Role of Rhythmic Neural Synchronization in Rest and Task Conditions

Gustavo Deco, Timothée Masquelier, Etienne Hugues, 2011, Front. Hum. Neurosci..

Object Categorization in Finer Levels Relies More on Higher Spatial Frequencies and Takes Longer

Matin N. Ashtiani, T. Masquelier, S. R. Kheradpisheh, 2017, Front. Psychol..

FACE FEATURE LEARNING WITH SPIKE TIMING DEPENDENT PLASTICITY

S. Thorpe, T. Masquelier, 2006 .

Oscillations,Phase-of-FiringCoding,andSpike Timing-DependentPlasticity:AnEfficientLearningScheme

T. Masquelier, 2009 .

Encrypted Internet traffic classification using a supervised Spiking Neural Network

T. Masquelier, C. A. Melchor, Romain Zimmer, 2021, Neurocomputing.

Spiking neural networks trained via proxy

T. Masquelier, S. R. Kheradpisheh, M. Mirsadeghi, 2021, IEEE Access.

Dilated convolution with learnable spacings

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2021, ArXiv.

Unsupervised learning of repeating patterns using a novel STDP based algorithm

Timothée Masquelier, Jacob G. Martin, Simon J. Thorpe, 2017 .

StereoSpike: Depth Learning With a Spiking Neural Network

T. Masquelier, B. Cottereau, Ulysse Rançon, 2021, IEEE Access.

Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing

Ismail Khalfaoui Hassani, T. Masquelier, S. R. Kheradpisheh, 2022, ArXiv.

Dilated convolution with learnable spacings

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2021, ArXiv.

BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning

T. Masquelier, S. R. Kheradpisheh, M. Mirsadeghi, 2021, Neural Processing Letters.

Analyzing time-to-first-spike coding schemes: A theoretical approach

S. Thorpe, T. Masquelier, J. Gautrais, 2022, Frontiers in Neuroscience.

Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation

Dennis G. Wilson, T. Masquelier, I. Hammouamri, 2022, Trans. Mach. Learn. Res..

Digital electronic circuit and system implementing a spiking neural network for performing unsupervised detection of patterns

Jacob Martin, Timothée Masquelier, Simon J. Thorpe, 2017 .

Learning simple and complex cells-like receptive fields from natural images: a plausibility proof

Tomaso Poggio, Timothée Masquelier, Simon J. Thorpe, 2010 .

S4NN: temporal backpropagation for spiking neural networks with one spike per neuron

T. Masquelier, S. R. Kheradpisheh, 2019, Int. J. Neural Syst..

Emergence of Binocular Disparity Selectivity through Hebbian Learning

Tushar Chauhan, Timothée Masquelier, Benoit R Cottereau, 2017, The Journal of Neuroscience.

First-Spike-Based Visual Categorization Using Reward-Modulated STDP

Timothée Masquelier, Milad Mozafari, Abbas Nowzari-Dalini, 2017, IEEE Transactions on Neural Networks and Learning Systems.

Representation Learning using Event-based STDP

Timothée Masquelier, Anthony S. Maida, Amirhossein Tavanaei, 2017, Neural Networks.

Network Bursting Dynamics in Excitatory Cortical Neuron Cultures Results from the Combination of Different Adaptive Mechanism

Gustavo Deco, Timothée Masquelier, G. Deco, 2013, PloS one.

Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder

Timothée Masquelier, Mohammad Ganjtabesh, Saeed Reza Kheradpisheh, 2016, Front. Comput. Neurosci..

Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition

Timothée Masquelier, Mohammad Ganjtabesh, Saeed Reza Kheradpisheh, 2015, Scientific Reports.

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, T. Masquelier, 2011, Journal of Computational Neuroscience.

Learning mechanisms to account for the speed, selectivity and invariance of responses in the visual cortex

Timothée Masquelier, T. Masquelier, 2008 .

Learning complex cell invariance from natural videos: A plausibility proof

Thomas Serre, Tomaso Poggio, Timothée Masquelier, 2007 .

Deep Learning in Spiking Neural Networks

Timothée Masquelier, Anthony S. Maida, Amirhossein Tavanaei, 2018, Neural Networks.

BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning pdf

Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timoth'ee Masquelier, 2020, Neural Processing Letters.

STDP and STDP variations with memristors for spiking neuromorphic learning systems

T. Serrano-Gotarredona, B. Linares-Barranco, T. Prodromakis, 2013, Front. Neurosci..

Microsaccades enable efficient synchrony-based coding in the retina: a simulation study

Pierre Kornprobst, Timothée Masquelier, Geoffrey Portelli, 2016, Scientific Reports.

On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex

Bernabé Linares-Barranco, Luis A. Camuñas-Mesa, Teresa Serrano-Gotarredona, 2011, Front. Neurosci..

Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks

Abbas Nowzari-Dalini, Simon J. Thorpe, Timothée Masquelier, 2019, Pattern Recognit..

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

Abbas Nowzari-Dalini, Timothée Masquelier, Milad Mozafari, 2019, Front. Neurosci..

Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition pdf

Simon J. Thorpe, Timothée Masquelier, Milad Mozafari, 2018, ArXiv.

Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks

Simon J. Thorpe, Milad Mozafari, Mohammad Ganjtabesh, 2018, Pattern Recognit..

Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity

Timothée Masquelier, Simon J. Thorpe, S. Thorpe, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

Yonghong Tian, T. Masquelier, Tiejun Huang, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Deep Residual Learning in Spiking Neural Networks

Yonghong Tian, T. Masquelier, Tiejun Huang, 2021, NeurIPS.

Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition pdf

Timothée Masquelier, Mohammad Ganjtabesh, Saeed Reza Kheradpisheh, 2015, Neurocomputing.

Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network

Timothée Masquelier, Pouya Soltani Zarrin, Christian Wenger, 2020, IWBBIO.

Acquisition of visual features through probabilistic spike-timing-dependent plasticity

Timothée Masquelier, Anthony S. Maida, Amirhossein Tavanaei, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings

Pierre Kornprobst, Luca Berdondini, Stefano Di Marco, 2016, eNeuro.

Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer

Timothée Masquelier, Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, 2017 .

Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme

Gustavo Deco, Timothée Masquelier, Etienne Hugues, 2009, The Journal of Neuroscience.

STiDi-BP: Spike time displacement based error backpropagation in multilayer spiking neural networks

Timothée Masquelier, Saeed Reza Kheradpisheh, Maryam Mirsadeghi, 2021, Neurocomputing.

Spike time displacement-based error backpropagation in convolutional spiking neural networks pdf

Timothée Masquelier, Saeed Reza Kheradpisheh, Maryam Mirsadeghi, 2021, Neural Computing and Applications.

Low-Activity Supervised Convolutional Spiking Neural Networks Applied to Speech Commands Recognition pdf

Thomas Pellegrini, Timoth'ee Masquelier, Romain Zimmer, 2020, 2021 IEEE Spoken Language Technology Workshop (SLT).

Training Spiking Neural Networks with Event-driven Backpropagation

T. Masquelier, Wei Fang, Zhaofei Yu, 2022, NeurIPS.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Romain D. Cazé, Alfred B. Yu, Derek K. Jones, 2017, BMC Neuroscience.

Supplementary Materials for: Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

Yonghong Tian, T. Masquelier, Wei Fang, 2021 .

STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons

Timothée Masquelier, T. Masquelier, 2016, Neuroscience.

Competitive STDP-Based Spike Pattern Learning

Timothée Masquelier, Simon J. Thorpe, Rudy Guyonneau, 2009, Neural Computation.

Spike-Timing-Dependent-Plasticity with Memristors

Bernabé Linares-Barranco, Teresa Serrano-Gotarredona, Timothée Masquelier, 2019, Handbook of Memristor Networks.

Learning and Coding in Neural Networks

Gustavo Deco, Timothée Masquelier, G. Deco, 2013 .

Neural variability, or lack thereof

Timothée Masquelier, T. Masquelier, 2013, Front. Comput. Neurosci..

The Timing of Vision – How Neural Processing Links to Different Temporal Dynamics

Gustavo Deco, Timothée Masquelier, Larissa Albantakis, 2011, Front. Psychology.

Oscillations can reconcile slowly changing stimuli with short neuronal integration and STDP timescales

Timothée Masquelier, T. Masquelier, 2014, Network.

Hardware implementation of convolutional STDP for on-line visual feature learning

Bernabé Linares-Barranco, Teresa Serrano-Gotarredona, Amirreza Yousefzadeh, 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

Dilated convolution with learnable spacings

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2021, ICLR.

Fast Threshold Optimization for Multi-Label Audio Tagging Using Surrogate Gradient Learning

Thomas Pellegrini, Timoth'ee Masquelier, T. Masquelier, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Visual stimulation quenches global alpha range activity in awake primate V4: a case study

Gustavo Deco, Timothée Masquelier, Guillaume S Masson, 2017, Neurophotonics.

Adapting a ConvNeXt model to audio classification on AudioSet

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2023, INTERSPEECH 2023.

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains

Matthieu Gilson, Timothée Masquelier, Etienne Hugues, 2011, PLoS Comput. Biol..

Optical flow estimation from event-based cameras and spiking neural networks

T. Masquelier, B. Cottereau, Ulysse Rançon, 2023, Frontiers in Neuroscience.

Dilated Convolution with Learnable Spacings: beyond bilinear interpolation

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2023, arXiv.org.

Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings

T. Masquelier, Ilyass Hammouamri, Ismail Khalfaoui-Hassani, 2023, ArXiv.

Event-Based Trajectory Prediction Using Spiking Neural Networks

Tushar Chauhan, Timothée Masquelier, Michel Paindavoine, 2021, Frontiers in Computational Neuroscience.

Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings

T. Masquelier, Ilyass Hammouamri, Ismail Khalfaoui-Hassani, 2023, ArXiv.

Design and evaluation of a GPS-aided communication device for railroad workers

Timothée Masquelier, T. Masquelier, 2001 .

Emergence of Binocular Disparity Selectivity through Hebbian Learning

T. Masquelier, B. Cottereau, Tushar Chauhan, 2017, The Journal of Neuroscience.

Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

S. Thorpe, T. Masquelier, 2007, PLoS Comput. Biol..

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, 2012, Journal of Computational Neuroscience.

Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection

Timothée Masquelier, Saeed Reza Kheradpisheh, T. Masquelier, 2018, Front. Comput. Neurosci..

Audio classification with Dilated Convolution with Learnable Spacings

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2023, ArXiv.

Dilated Convolution with Learnable Spacings: beyond bilinear interpolation

Ismail Khalfaoui Hassani, T. Masquelier, Thomas Pellegrini, 2023, arXiv.org.

Deep Learning in Spiking Neural Networks

T. Masquelier, S. R. Kheradpisheh, M. Ghodrati, 2018, Neural Networks.

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

T. Masquelier, Wei Fang, Zhaofei Yu, 2023, Science advances.

Deep Residual Learning in Spiking Neural Networks

Tiejun Huang, Yonghong Tian, Zhaofei Yu, 2021, NeurIPS.

Representation Learning using Event-based STDP

T. Masquelier, A. Maida, A. Tavanaei, 2017, Neural Networks.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Romain D. Cazé, Alfred B. Yu, Derek K. Jones, 2017, BMC Neuroscience.