1.4k citations · 3.9k across the 36 of their papers we have counts for
39 papers
Binary Spiking Neural Networks as Causal Models
Aditya Kar, Emiliano Lorini, Timothée Masquelier
We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking activity as a binary causal mo…
DelRec: learning delays in recurrent spiking neural networks
Alexandre Queant, Ulysse Rançon, Benoit R Cottereau +1
Biological neurons transmit information with stereotyped electrical impulses called ``spikes'', sensitive to coincident timings. Spiking Neural Networks (SNNs), introduced in the n…
Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
Peng Xue, Wei Fang, Zhengyu Ma +5
Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their complex neuronal dynamics and sparse binary activations (spikes) inspired by the b…
Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
Kaiwei Che, Zhengyu Ma, Yifan Huang +5
Spiking Neural Networks (SNNs), with their event-driven and biologically inspired mechanisms, are well-suited for energy-efficient neuromorphic hardware. Neural coding, which is cr…
Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study
Rabih Chamas, Ismail Khalfaoui-Hassani, Timothee Masquelier
Dilated Convolution with Learnable Spacing (DCLS) is a recent advanced convolution method that allows enlarging the receptive fields (RF) without increasing the number of parameter…
Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning
Alireza Nadafian, Milad Mozafari, Timothée Masquelier +1
The growing interest in brain-inspired computational models arises from the remarkable problem-solving efficiency of the human brain. Action recognition, a complex task in computat…
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Wei Fang, Yanqi Chen, Jianhao Ding +7
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As…
Audio classification with Dilated Convolution with Learnable Spacings
Ismail Khalfaoui-Hassani, Timothée Masquelier, Thomas Pellegrini
Dilated convolution with learnable spacings (DCLS) is a recent convolution method in which the positions of the kernel elements are learned throughout training by backpropagation.…
Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings
Ilyass Hammouamri, Ismail Khalfaoui-Hassani, Timothée Masquelier
Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognit…
Adapting a ConvNeXt model to audio classification on AudioSet
Thomas Pellegrini, Ismail Khalfaoui-Hassani, Etienne Labbé +1
In computer vision, convolutional neural networks (CNN) such as ConvNeXt, have been able to surpass state-of-the-art transformers, partly thanks to depthwise separable convolutions…
Dilated Convolution with Learnable Spacings: beyond bilinear interpolation
Ismail Khalfaoui-Hassani, Thomas Pellegrini, Timothée Masquelier
Dilated Convolution with Learnable Spacings (DCLS) is a recently proposed variation of the dilated convolution in which the spacings between the non-zero elements in the kernel, or…
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Wei Fang, Zhaofei Yu, Zhaokun Zhou +5
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies.…
Optical flow estimation from event-based cameras and spiking neural networks
Javier Cuadrado, Ulysse Rançon, Benoît Cottereau +2
Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance chan…
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Alireza Azadbakht, Saeed Reza Kheradpisheh, Ismail Khalfaoui-Hassani +1
Deep convolutional neural networks (DCNNs) have become the state-of-the-art (SOTA) approach for many computer vision tasks: image classification, object detection, semantic segment…
Dilated convolution with learnable spacings
Ismail Khalfaoui-Hassani, Thomas Pellegrini, Timothée Masquelier
Recent works indicate that convolutional neural networks (CNN) need large receptive fields (RF) to compete with visual transformers and their attention mechanism. In CNNs, RFs can…
StereoSpike: Depth Learning with a Spiking Neural Network
Ulysse Rançon, Javier Cuadrado-Anibarro, Benoit R. Cottereau +1
Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using a…
Spiking neural networks trained via proxy
Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
We propose a new learning algorithm to train spiking neural networks (SNN) using conventional artificial neural networks (ANN) as proxy. We couple two SNN and ANN networks, respect…
Spike time displacement based error backpropagation in convolutional spiking neural networks
Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh +1
We recently proposed the STiDi-BP algorithm, which avoids backward recursive gradient computation, for training multi-layer spiking neural networks (SNNs) with single-spike-based t…
Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning
Thomas Pellegrini, Timothée Masquelier
Multi-label audio tagging consists of assigning sets of tags to audio recordings. At inference time, thresholds are applied on the confidence scores outputted by a probabilistic cl…
Deep Residual Learning in Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Conside…
Encrypted Internet traffic classification using a supervised Spiking Neural Network
Ali Rasteh, Florian Delpech, Carlos Aguilar-Melchor +3
Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them def…
Low-activity supervised convolutional spiking neural networks applied to speech commands recognition
Thomas Pellegrini, Romain Zimmer, Timothée Masquelier
Deep Neural Networks (DNNs) are the current state-of-the-art models in many speech related tasks. There is a growing interest, though, for more biologically realistic, hardware fri…
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility.…
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and…