1.4k citations · 3.9k across the 36 of their papers we have counts for
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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…