122 citations · 248 across the 17 of their papers we have counts for
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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…