Research map / Architectures and efficiency / Spiking networks
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train SNN with TSSL-BP vs STBP | spiking CNN 1 · CIFAR10 | accuracy: 85.24% -> 89.22% | Helped |
| TSSL-BP vs STBP with NeuNorm | spiking CNN 2 · CIFAR10 | accuracy: 90.53% -> 91.41% | Helped |
| TSSL-BP (5 steps) vs ST-RSBP (400 steps) | spiking CNN · MNIST | accuracy: 99.62% -> 99.53% | Hurt |
| TSSL-BP spiking CNN vs same-size non-spiking ANN | CNN · FashionMNIST | accuracy: 91.60% -> 92.83% | Helped |
| reduce time steps from 100 to 30 (ablation) | TSSL-BP · N-MNIST | accuracy: 99.40% -> 99.28% | Hurt |
Closest papers by meaning
- Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation (NeurIPS 2024 · similarity 0.91)
- TS-SNN: Temporal Shift Module for Spiking Neural Networks (ICML 2025 · similarity 0.90)
- Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training (NeurIPS 2025 · similarity 0.89)
- Training High Performance Spiking Neural Network by Temporal Model Calibration (ICML 2025 · similarity 0.89)
- Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness (ICLR 2025 · similarity 0.89)
- NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks (ICML 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.