mlsanityData to 14 Oct 2026

Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns

arXiv 2301.12356 ↗First posted 29 Jan 2023cs.NE cs.CVcited by 3

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Abstract (arXiv)

Spiking Neural Networks (SNNs) use discrete spike sequences to transmit information, which significantly mimics the information transmission of the brain. Although this binarized form of representation dramatically enhances the energy efficiency and robustness of SNNs, it also leaves a large gap between the performance of SNNs and Artificial Neural Networks based on real values. There are many different spike patterns in the brain, and the dynamic synergy of these spike patterns greatly enriches the representation capability. Inspired by spike patterns in biological neurons, this paper introduces the dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (LIFB) neuron that can make a trade-off between short-time performance and dynamic temporal performance from the perspective of network information capacity. LIFB neuron exhibits three modes, resting, Regular spike, and Burst spike. The burst density of the neuron can be adaptively adjusted, which significantly enriches the characterization capability. We also propose a decoupling method that can losslessly decouple LIFB neurons into equivalent LIF neurons, which demonstrates that LIFB neurons can be efficiently implemented on neuromorphic hardware. We conducted experiments on the static datasets CIFAR10, CIFAR100, and ImageNet, which showed that we greatly improved the performance of the SNNs while significantly reducing the network latency. We also conducted experiments on neuromorphic datasets DVS-CIFAR10 and NCALTECH101 and showed that we achieved state-of-the-art with a small network structure.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 36 of its 66 references that are in the map. First posted on arXiv 29 Jan 2023.

Builds on

36 of 66 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  6. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  7. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  8. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  9. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  10. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  11. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  12. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 233
24 more
  1. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  2. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 144
  3. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 115
  4. 2022Attention Spiking Neural Networkscited by 107
  5. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
  6. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 100
  7. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 80
  8. 2020DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networkscited by 71
  9. 2020LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processingcited by 56
  10. 2022Neural Architecture Search for Spiking Neural Networkscited by 53
  11. 2017DART: Distribution Aware Retinal Transform for Event-based Camerascited by 40
  12. 2022AutoSNN: Towards Energy-Efficient Spiking Neural Networkscited by 40
  13. 2018Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networkscited by 35
  14. 2022TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networkscited by 35
  15. 2022BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulationcited by 33
  16. 2022Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikescited by 30
  17. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 23
  18. 2021BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neuronscited by 22
  19. 2021Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learningcited by 22
  20. 2020Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemescited by 17
  21. 2019SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikescited by 16
  22. 2021BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neuronscited by 12
  23. 2021Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsulescited by 6
  24. 2020Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Trainingcited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  2. 2024Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagationcited by 8
  3. 2025Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deploymentcited by 2