Timothée Masquelier

Orcid: 0000-0001-8629-9506

According to our database1, Timothée Masquelier authored at least 58 papers between 2007 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses.
PLoS Comput. Biol., 2024

ETTFS: An Efficient Training Framework for Time-to-First-Spike Neuron.
CoRR, 2024

Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study.
CoRR, 2024

Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning.
CoRR, 2024

Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.
J. Comput. Neurosci., November, 2023

Spike time displacement-based error backpropagation in convolutional spiking neural networks.
Neural Comput. Appl., July, 2023

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.
CoRR, 2023

Audio classification with Dilated Convolution with Learnable Spacings.
CoRR, 2023

Dilated Convolution with Learnable Spacings: beyond bilinear interpolation.
CoRR, 2023

Parallel Spiking Neurons with High Efficiency and Long-term Dependencies Learning Ability.
CoRR, 2023

Optical Flow estimation with Event-based Cameras and Spiking Neural Networks.
CoRR, 2023

Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adapting a ConvNeXt Model to Audio Classification on AudioSet.
Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

Dilated convolution with learnable spacings.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation.
Trans. Mach. Learn. Res., 2022

BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning.
Neural Process. Lett., 2022

Encrypted internet traffic classification using a supervised spiking neural network.
Neurocomputing, 2022

Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing.
CoRR, 2022

StereoSpike: Depth Learning With a Spiking Neural Network.
IEEE Access, 2022

Spiking Neural Networks Trained via Proxy.
IEEE Access, 2022

Training Spiking Neural Networks with Event-driven Backpropagation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
STiDi-BP: Spike time displacement based error backpropagation in multilayer spiking neural networks.
Neurocomputing, 2021

Event-Based Trajectory Prediction Using Spiking Neural Networks.
Frontiers Comput. Neurosci., 2021

Back-propagation Now Works in Spiking Neural Networks!.
ERCIM News, 2021

Spike-based Residual Blocks.
CoRR, 2021

Low-Activity Supervised Convolutional Spiking Neural Networks Applied to Speech Commands Recognition.
Proceedings of the IEEE Spoken Language Technology Workshop, 2021

Deep Residual Learning in Spiking Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Fast Threshold Optimization for Multi-Label Audio Tagging Using Surrogate Gradient Learning.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron.
Int. J. Neural Syst., 2020

Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network.
Proceedings of the Bioinformatics and Biomedical Engineering, 2020

2019
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks.
Pattern Recognit., 2019

Deep learning in spiking neural networks.
Neural Networks, 2019

Technical report: supervised training of convolutional spiking neural networks with PyTorch.
CoRR, 2019

S4NN: temporal backpropagation for spiking neural networks with one spike per neuron.
CoRR, 2019

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron.
CoRR, 2019

Spike-Timing-Dependent-Plasticity with Memristors.
Proceedings of the Handbook of Memristor Networks., 2019

2018
First-Spike-Based Visual Categorization Using Reward-Modulated STDP.
IEEE Trans. Neural Networks Learn. Syst., 2018

Representation learning using event-based STDP.
Neural Networks, 2018

STDP-based spiking deep convolutional neural networks for object recognition.
Neural Networks, 2018

Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing.
Frontiers Neuroinformatics, 2018

Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection.
Frontiers Comput. Neurosci., 2018

Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition.
CoRR, 2018

2017
Hardware implementation of convolutional STDP for on-line visual feature learning.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2017

Live demonstration: Hardware implementation of convolutional STDP for on-line visual feature learning.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2017

2016
Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition.
Neurocomputing, 2016

Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.
Frontiers Comput. Neurosci., 2016

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

STDP-based spiking deep neural networks for object recognition.
CoRR, 2016

Acquisition of visual features through probabilistic spike-timing-dependent plasticity.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015
Deep Networks Resemble Human Feed-forward Vision in Invariant Object Recognition.
CoRR, 2015

2013
Neural variability, or lack thereof.
Frontiers Comput. Neurosci., 2013

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

2011
STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains.
PLoS Comput. Biol., 2011

2010
Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity.
Proceedings of the International Joint Conference on Neural Networks, 2010

2009
Competitive STDP-Based Spike Pattern Learning.
Neural Comput., 2009

2007
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity.
PLoS Comput. Biol., 2007


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