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