mlsanityData to 7 Oct 2026

Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

arXiv 1809.03142 ↗First posted 10 Sep 2018cs.NE cs.LG q-bio.NCcited by 34

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Abstract (arXiv)

The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 7; 2023: 9; 2024: 3; 2025: 3; 2026: 4.

34 papers.

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  1. 2022GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networkscited by 63
  2. 2022AutoSNN: Towards Energy-Efficient Spiking Neural Networkscited by 40
  3. 2021SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environmentscited by 29
  4. 2021Visual Explanations from Spiking Neural Networks using Interspike Intervalscited by 26
  5. 2022Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?cited by 24
  6. 2022Real Spike: Learning Real-valued Spikes for Spiking Neural Networkscited by 22
  7. 2021ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memoriescited by 20
  8. 2022lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agentscited by 18
  9. 2021One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latencycited by 17
  10. 2021Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networkscited by 16
  11. 2022Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentationcited by 15
  12. 2021BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neuronscited by 12
22 more
  1. 2023Biologically inspired structure learning with reverse knowledge distillation for spiking neural networkscited by 11
  2. 2025Differential Coding for Training-Free ANN-to-SNN Conversioncited by 9
  3. 2024Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applicationscited by 7
  4. 2023EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systemscited by 6
  5. 2024Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networkcited by 6
  6. 2021Noise-Robust Deep Spiking Neural Networks with Temporal Informationcited by 5
  7. 2023Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patternscited by 3
  8. 2023Ultra-low-power Image Classification on Neuromorphic Hardwarecited by 3
  9. 2024Exploring the Limitations of Layer Synchronization in Spiking Neural Networkscited by 3
  10. 2023Highly Efficient SNNs for High-speed Object Detectioncited by 2
  11. 2023SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connectioncited by 2
  12. 2021Continual learning under domain transfer with sparse synaptic burstingcited by 1
  13. 2022SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Drivingcited by 1
  14. 2025IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processingcited by 1
  15. 2025Dynamical Alignment: A Principle for Adaptive Neural Computationcited by 1
  16. 2023InfLoR-SNN: Reducing Information Loss for Spiking Neural Networkscited by 0
  17. 2023Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networkscited by 0
  18. 2023When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networkscited by 0
  19. 2026Error Amplification Limits ANN-to-SNN Conversion in Continuous Controlcited by 0
  20. 2026Learnable Burst Quantization for Expressive and Efficient Spiking Neural Networkscited by 0
  21. 2026PSViT: A Methodology for Structurally Pruning Spiking Vision Transformerscited by 0
  22. 2026Event Burst Trigger: An Availability Backdoor Attack on Event-Based SNN Object Detectioncited by 0