1.4k citations · 3.9k across the 36 of their papers we have counts for
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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…
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…
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…
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.…
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…
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…
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…
Technical report: supervised training of convolutional spiking neural networks with PyTorch
Romain Zimmer, Thomas Pellegrini, Srisht Fateh Singh +1
Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly us…
S4NN: temporal backpropagation for spiking neural networks with one spike per neuron
Saeed Reza Kheradpisheh, Timothée Masquelier
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all…
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini +1
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and en…
Deep Learning in Spiking Neural Networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh +2
In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural ne…
Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection
Timothée Masquelier, Saeed Reza Kheradpisheh
Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding),…
Representation Learning using Event-based STDP
Amirhossein Tavanaei, Timothee Masquelier, Anthony Maida
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a ch…
STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons
Timothée Masquelier
By recording multiple cells simultaneously, electrophysiologists have found evidence for repeating spatiotemporal spike patterns, which can carry information. How this information…
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
Amirhossein Tavanaei, Timothee Masquelier, Anthony S Maida
The final version of this paper has been published in IEEEXplore available at http://ieeexplore.ieee.org/document/7727213. Please cite this paper as: Amirhossein Tavanaei, Timothee…