Papers › IM-Loss: Information Maximization Loss for Spiking Neural Networks
IM-Loss: Information Maximization Loss for Spiking Neural Networks
Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, YingLei Wang, Xuhui Huang, Zhe Ma
Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. It transmits information by 0/1 spikes. This bio-mimetic mechanism of SNN demonstrates extreme energy efficiency since it avoids any multiplications on neuromorphic hardware. However, the forward-passing 0/1 spike quantization will cause information loss and accuracy degradation. To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper. The IM-Loss not only enhances the information expressiveness of an SNN directly but also plays a part of the role of normalization without introducing any additional operations (e.g., bias and scaling) in the inference phase. Additionally, we introduce a novel differentiable spike activity estimation, Evolutionary Surrogate Gradients (ESG) in SNNs. By appointing automatic evolvable surrogate gradients for spike activity function, ESG can ensure sufficient model updates at the beginning and accurate gradients at the end of the training, resulting in both easy convergence and high task performance. Experimental results on both popular non-spiking static and neuromorphic datasets show that the SNN models trained by our method outperform the current state-of-the-art algorithms.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Event data classification | CIFAR10-DVS | IM-Loss (ResNet-19) | Accuracy | 72.60 | #6 of 7 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | IM-Loss (ResNet-19) | Percentage correct | 95.49 | #134 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | IM-Loss (VGG-16) | Percentage correct | 70.18 | #176 of 211 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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