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GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

NeurIPS 2022 · Architectures and efficiency › Spiking networks · approach: Bio-inspired spiking neuron models · 45 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace LIF neurons with GLIF in STBP-tdBN ResNet-19STBP-tdBN · CIFAR-100 T=6top-1 accuracy: 71.12 -> 77.35Helped
GLIF vs TETResNet-19 · CIFAR-10 T=6top-1 accuracy: 94.50 -> 95.03Helped
GLIF vs STBP-tdBN on ImageNetResNet-34 · ImageNettop-1 accuracy: 63.72 -> 69.09Helped
GLIF on MS-ResNet-18MS-ResNet-18 · ImageNettop-1 accuracy: 63.10 -> 68.11Helped
fuse dual bio-features with gating instead of single simplex LIF (best simplex 100) (ablation)GLIF · CIFAR-100 ResNet-19 T=4top-1 accuracy: 77.15 -> 77.22Helped
fuse dual bio-features with gating instead of simplex LIF 000 (ablation)GLIF · CIFAR-100 ResNet-18 T=4top-1 accuracy: 75.77 -> 76.39Helped
make gating factors learnable instead of static (ablation)GLIF · CIFAR-100 ResNet-19 T=4top-1 accuracy: 76.89 -> 77.22Helped
use gating instead of simple addition fusion (ablation)GLIF · CIFAR-100 ResNet-19 T=4top-1 accuracy: 71.26 -> 77.22Helped
channel-wise instead of layer-wise parameter sharing (ablation)GLIF · CIFAR-100 ResNet-19 T=4top-1 accuracy: 75.71 -> 77.22Helped
channel-wise instead of layer-wise parameter sharing (ablation)GLIF · CIFAR-100 ResNet-18 T=2top-1 accuracy: 73.40 -> 74.85Helped

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