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

arXiv: https://arxiv.org/abs/1809.03142 · HTML: https://mlsanity.com/paper/1809.03142/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 30 of 35)

- [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022)
- [AutoSNN: Towards Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2201.12738/) (2022)
- [SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments](https://mlsanity.com/paper/2103.00424/) (2021)
- [Visual Explanations from Spiking Neural Networks using Interspike Intervals](https://mlsanity.com/paper/2103.14441/) (2021)
- [Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?](https://mlsanity.com/paper/2202.03133/) (2022)
- [Real Spike: Learning Real-valued Spikes for Spiking Neural Networks](https://mlsanity.com/paper/2210.06686/) (2022)
- [Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks](https://mlsanity.com/paper/2104.12528/) (2021)
- [ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories](https://mlsanity.com/paper/2108.10271/) (2021)
- [lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents](https://mlsanity.com/paper/2205.12295/) (2022)
- [One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency](https://mlsanity.com/paper/2110.05929/) (2021)
- [Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation](https://mlsanity.com/paper/2207.02702/) (2022)
- [BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons](https://mlsanity.com/paper/2105.12917/) (2021)
- [Energy-efficient Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2106.07172/) (2021)
- [Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks](https://mlsanity.com/paper/2304.09500/) (2023)
- [Differential Coding for Training-Free ANN-to-SNN Conversion](https://mlsanity.com/paper/2503.00301/) (2025)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems](https://mlsanity.com/paper/2304.04039/) (2023)
- [Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network](https://mlsanity.com/paper/2412.16219/) (2024)
- [Noise-Robust Deep Spiking Neural Networks with Temporal Information](https://mlsanity.com/paper/2104.11169/) (2021)
- [Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns](https://mlsanity.com/paper/2301.12356/) (2023)
- [Ultra-low-power Image Classification on Neuromorphic Hardware](https://mlsanity.com/paper/2309.16795/) (2023)
- [Exploring the Limitations of Layer Synchronization in Spiking Neural Networks](https://mlsanity.com/paper/2408.05098/) (2024)
- [Highly Efficient SNNs for High-speed Object Detection](https://mlsanity.com/paper/2309.15883/) (2023)
- [SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection](https://mlsanity.com/paper/2311.06570/) (2023)
- [Continual learning under domain transfer with sparse synaptic bursting](https://mlsanity.com/paper/2108.12056/) (2021)
- [SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving](https://mlsanity.com/paper/2206.02876/) (2022)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Dynamical Alignment: A Principle for Adaptive Neural Computation](https://mlsanity.com/paper/2508.10064/) (2025)
- [Error Amplification Limits ANN-to-SNN Conversion in Continuous Control](https://mlsanity.com/paper/2601.21778/) (2026)
- [InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks](https://mlsanity.com/paper/2307.04356/) (2023)

## 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.