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https://github.com/TheBrainLab/Awesome-Spiking-Neural-Networks

A paper list of spiking neural networks, including papers, codes, and related websites. 本仓库收集脉冲神经网络相关的顶会顶刊以及CNS论文和代码，正在持续更新中。
https://github.com/TheBrainLab/Awesome-Spiking-Neural-Networks

List: Awesome-Spiking-Neural-Networks

3rd-generation-of-artificial-neural-networks awesome awesome-list binary biological-plausibility brain-inspired codes deep-learning energy-efficiency event-driven neuromorphic-computing paperlist papers snn spike spiking spiking-neural-network spikingjelly

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A paper list of spiking neural networks, including papers, codes, and related websites. 本仓库收集脉冲神经网络相关的顶会顶刊以及CNS论文和代码，正在持续更新中。

Awesome Lists containing this project

README 

          
# Awesome Spiking Neural Networks[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



Collect some spiking neural network papers & codes.  (**Actively keep updating**)



If you own or find some overlooked SNN papers, you can add them to this document by pull request. 



## News



 News 2025 



[2025.11.01] Update SNN-related papers in Nature, Science 2025 (10 papers).



[2025.10.31] Update SNN-related papers in Neurips 2025 (30 papers).



[2025.05.26] Update SNN-related papers in ICML 2025 (18 papers), IJCAI (11 papers), ICCV (7 papers), and ACM MM (9 papers).



[2025.04.11] Update SNN-related papers in ICLR 2025 (11 papers), CVPR 2025 (14 papers).



[2025.02.06] Update SNN-related papers in AAAI 2025 (18 papers).



 News 2024 



[2024.11.13] Update SNN-related papers in NeurIPS 2024 (18 papers).



[2024.10.31] Update SNN-related papers in ACM MM 2024 (5 papers).



[2024.10.15] Update SNN-related papers in ECCV 2024 (8 papers).



[2024.05.29] Update SNN-related papers in ICML 2024 (13 papers), IJCAI 2024 (5).



[2024.04.29] Update SNN-related papers in ICLR 2024 (17 papers), AAAI 2024 (8), CVPR 2024 (3).



 News 2023 



[2023.12.31] Update SNN-related papers in TPAMI 2023, Frontiers in Neuroscience 2023.



[2023.10.31] Update SNN-related papers in CVPR 2023 (2 papers), ICML 2023 (2), IJCAI 2023 (3), and ICCV 2023 (10), NeurIPS 2023 (12).



[2023.06.25] Update SNN-related papers in ICLR 2023 (6 papers), AAAI 2023 (6 papers).



## Papers



### 2025



**Nature, Science, Cell**

- A spiking artificial neuron based on one diffusive memristor, one transistor and one resistor (**Nature Electronics, 2025**). [[paper](https://www.nature.com/articles/s41928-025-01488-x)][[code](https://github.com/GnohzZ/Brain-Dynamics-Modeling-Acceleration)]

- A biologically inspired artificial neuron with intrinsic plasticity based on monolayer molybdenum disulfide (**Nature Electronics, 2025**). [[paper](https://www.nature.com/articles/s41928-025-01433-y)]

- Modeling macroscopic brain dynamics with brain-inspired computing architecture (**Nature Communications, 2025**). [[paper](https://www.nature.com/articles/s41467-025-64470-3)]

- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting (**Nature Communications, 2025**). [[paper](https://www.nature.com/articles/s41467-025-64231-2)][[code](https://codeocean.com/capsule/9829487/tree)]

- Efficient and robust temporal processing with neural oscillations modulated spiking neural networks (**Nature Communications, 2025**). [[paper](https://www.nature.com/articles/s41467-025-63771-x)][[code](https://github.com/YinsongYan/Rhythm-SNN)]

- Covariant spatio-temporal receptive fields for spiking neural networks (**Nature Communications, 2025**). [[paper](https://www.nature.com/articles/s41467-025-63493-0)][[code](https://github.com/jegp/nrf)]

- A multisynaptic spiking neuron for simultaneously encoding spatial and temporal dynamics in spiking neural networks (**Nature Communications, 2025**). [[paper](https://doi.org/10.1038/s41467-025-62251-6)][[code](https://github.com/fanliangwei/Multisynaptic-spiking-neurons)]

- Advancing spatio-temporal processing through adaptation in spiking neural networks (**Nature Communications, 2025**). [[paper](https://www.nature.com/articles/s41467-025-60878-z)][[code](https://github.com/IGITUGraz/SE-adlif)]

- Topology optimization of random memristors for input-aware dynamic SNN (**Science Advances, 2025**). [[paper](https://www.science.org/doi/10.1126/sciadv.ads5340)][[code](https://github.com/bo-wang-up/PRIME)]

- Fully memristive spiking neural network for energy-efficient graph learning (**Science Advances, 2025**). [[paper](https://www.science.org/doi/10.1126/sciadv.adv2312)]

  

**AAAI, ICLR, CVPR, ICML, IJCAI, ICCV, ACM MM, Neurips**

- Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=oGmROC4e4W)] [[code](https://github.com/korneelf1/SpikingCrazyflie)]

- Toward Relative Positional Encoding in Spiking Transformers (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=MDWJlTWZHH)] [[code](https://github.com/microsoft/SeqSNN)]

- High Dynamic Range Imaging with Time-Encoding Spike Camera (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=flIdch9eTf)] [[code](https://github.com/zkzhu123/TESC)]

- Bipolar Self-attention for Spiking Transformers (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=nG45z7lJ7D)] [[code](https://openreview.net/attachment?id=nG45z7lJ7D&name=supplementary_material)]

- Spike-timing-dependent Hebbian learning as noisy gradient descent (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=YTbLri0siT)]

- Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=8W8SRZIpJP)]

- SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=Di0RasgbQ6)] [[code](https://github.com/ethanxyluo/SPACE)]

- MI-TRQR: Mutual Information-Based Temporal Redundancy Quantification and Reduction for Energy-Efficient Spiking Neural Networks (**Neurips  2025**). [[paper](https://openreview.net/pdf?id=NRqGpUAjV9)] [[code](https://github.com/dfxue/MI-TRQR)]

- Spik-NeRF: Spiking Neural Networks for Neural Radiance Fields (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=047VzZEpnu)] 

- Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=ywzGKDStrm)] [[code](https://openreview.net/attachment?id=ywzGKDStrm&name=supplementary_material)]

- Spiking Neural Networks Need High-Frequency Information (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=owNPAl7LNK)] [[code](https://github.com/bic-L/MaxFormer)]

- Activity Pruning for Efficient Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=zjOXZEXQKZ)] [[code](https://github.com/putshua/Activity-Pruning-SNN)]

- Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=4q5ZYP0ynu)] [[code](https://github.com/PengXue0812/Multiplication-Free-Parallelizable-Spiking-Neurons-with-Efficient-Spatio-Temporal-Dynamics)]

- SpikingVTG: A Spiking Detection Transformer for Video Temporal Grounding (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=SkhF3cuyev)] [[code](https://openreview.net/attachment?id=SkhF3cuyev&name=supplementary_material)]

- S$^2$M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=WtMuGdHvh6)] [[code](https://github.com/JackieWang9811/S2M-Former)]

- Local-Global Coupling Spiking Graph Transformer for Brain Disorders Diagnosis from Two Perspectives (**Neurips 2025**).  [[paper](https://openreview.net/pdf?id=kkhRTTmXFV)]

- A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=oAbaGU9N1X)] [[code](https://spikachu-bci.github.io/)]

- Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=VaE33hkqmg)] [[code](https://github.com/XY-boy/SpikeSR)]

- Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=2zZzdAMyYi)] [[code](https://github.com/JiangYizhou16/Adaptive-Fission)]

- S$^2$NN: Sub-bit Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=hFsCuVc1cB)] [[code](https://openreview.net/attachment?id=hFsCuVc1cB&name=supplementary_material)]

- Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=d6WUTRJqP3)] [[code](https://github.com/L-0cean/Fly-SNN)]

- Fully Spiking Neural Networks for Unified Frame-Event Object Tracking (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=FooiwsnEH9)] [[code](https://github.com/Noctis-A/SpikeFET)]

- Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=dpmMg6aK1D)] [[code](openreview.net/pdf?id=dpmMg6aK1D)]

- Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=fllsm01JWS)] 

- HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=YYz4fumVed)] [[code](https://github.com/dzcgood/HetSyn)]

- Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=tYnJC5ba6j)] [[code](https://github.com/EricZhang1412/Spatial-temporal-ERF)]

- Brain-like Variational Inference (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=573IcLusXq)] [[code](https://github.com/hadivafaii/IterativeVAE)]

- Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=RRBve5GwjS)] [[code](openreview.net/pdf?id=RRBve5GwjS)]

- Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=BrmR69AhUg)]

- Spike4DGS: Towards High-Speed Dynamic Scene Recontruction with 4D Gaussian Splatting via a Spike Camera Array (**Neurips 2025**). [[paper](https://openreview.net/pdf?id=V5efEA8nIr)] [[code](https://github.com/Qinghongye/Spike4DGS)]

- Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks (**KDD 2025**). [[paper](https://doi.org/10.48550/arXiv.2408.11067)][[code](https://github.com/yqding326/MRA-SNN)]

- DSF-Net: Dynamic Sparse Fusion of Event-RGB via Spike-Triggered Attention for High-Speed Detection (**ACM MM 2025**). 

- ESOD: Event-Based Small Object Detection (**ACM MM 2025**). 

- E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras (**ACM MM 2025**). 

- Incorporating the Refractory Period into Spiking Neural Networks through Spike-Triggered Threshold Dynamics (**ACM MM 2025**). 

- Signal-SGN: A Spiking Graph Convolutional Network for Skeleton Action Recognition via Learning Temporal-Frequency Dynamics (**ACM MM 2025**). 

- SGM-Transformer: Rethinking Gradient Information Loss and Compensation in Spiking Neural Networks (**ACM MM 2025**). 

- Advanced SpikingYOLOX: Extending Spiking Neural Network on Object Detection with Spike-based Partial Self-Attention and 2D-Spiking Transformer (**ACM MM 2025**). 

- Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning (**ACM MM 2025**). 

- Temporal-coded Spiking Transformer (**ACM MM 2025**). 

- ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/1849)]  

- Robust Unfolding Network for HDR Imaging with Modulo Cameras (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/1986)]    

- SpikeDiff: Zero-shot High-Quality Video Reconstruction from Chromatic Spike Camera and Sub-millisecond Spike Streams (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/1181)]    

- Noise-Modeled Diffusion Models for Low-Light Spike Image Restoration (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/1467)]    

- Efficient Spiking Point Mamba for Point Cloud Analysis (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/2271)]    

- SpikePack: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/345)]   

- SpiLiFormer: Enhancing Spiking Transformers with Lateral Inhibition (**ICCV 2025**). [[paper](https://iccv.thecvf.com/virtual/2025/poster/1753)]   

- Adaptive Gradient Learning for Spiking Neural Networks by Exploiting Membrane Potential Dynamics (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7783.pdf)]  

- ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/4566.pdf)]  

- Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7408.pdf)]  

- MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/7378.pdf)] 

- A Fast and Accurate ANN-SNN Conversion Algorithm with Negative Spikes (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/6149.pdf)] 

- ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3167.pdf)] 

- Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/3150.pdf)]

- SCNNs: Spike-based Coupling Neural Networks for Understanding Structural-Functional Relationships in the Human Brain (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/1337.pdf)]

- Exploiting Label Skewness for Spiking Neural Networks in Federated Learning (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/1298.pdf)]

- Binary Event-Driven Spiking Transformer (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/1206.pdf)]

- Tackling Long-Tailed Data Challenges in Spiking Neural Networks via Heterogeneous Knowledge Distillation (**IJCAI 2025**). [[paper](https://ijcai-preprints.s3.us-west-1.amazonaws.com/2025/787.pdf)]

- SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity (**ICML 2025**). [[paper](https://arxiv.org/abs/2505.10352)]

- Efficient ANN-SNN Conversion with Error Compensation Learning (**ICML 2025**). [[paper](https://www.arxiv.org/abs/2506.01968)]

- Differential Coding for Training-Free ANN-to-SNN Conversion (**ICML 2025**). [[paper](https://arxiv.org/abs/2503.00301)] 

- Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/44825)] 

- ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/43640)] 

- TTFSFormer: A TTFS-based Lossless Conversion of Spiking Transformer (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/44159)] 

- BSO: Binary Spiking Online Optimization (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/45087)] 

- Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/43816)] 

- TS-SNN: Temporal Shift Module for Spiking Neural Networks (**ICML 2025**). [[paper](https://arxiv.org/abs/2505.04165)] 

- SpikF: Spiking Fourier Network for Efficient Long-term Prediction (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/46411)] 

- Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning (**ICML 2025**). [[paper](https://arxiv.org/pdf/2505.07921)] 

- Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/44986)] 

- Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/45776)] 

- Training High Performance Spiking Neural Networks by Temporal Model Calibration (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/44216)] 

- Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons (**ICML 2025**). [[paper](https://icml.cc/virtual/2025/poster/45627)] 

- Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time (**ICML 2025**). [[paper](https://arxiv.org/abs/2505.18023)] 

- Hybrid Spiking Vision Transformer for Object Detection with Event Cameras (**ICML 2025**). [[paper](https://arxiv.org/abs/2505.07715)] 

- Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model (**ICML 2025**). [[paper](https://arxiv.org/abs/2409.15298)] 

- EventGPT: Event Stream Understanding with Multimodal Large Language Models (**CVPR 2025**). [[paper](https://arxiv.org/pdf/2412.00832)]  [[code](https://github.com/XduSyL/EventGPT)]

- Spk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering (**CVPR 2025**). [[paper](https://cvpr.thecvf.com/virtual/2025/poster/33079)]

- Decision SpikeFormer: Spike-Driven Transformer for Decision Making (**CVPR 2025**). [[paper](https://cvpr.thecvf.com/virtual/2025/poster/32864)]

- Self-Supervised Learning for Color Spike Camera Reconstruction (**CVPR 2025**). [[paper](https://cvpr.thecvf.com/virtual/2025/poster/34093)]

- USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting (**CVPR 2025**). [[paper](https://arxiv.org/abs/2411.10504)]

- VISTREAM: Improving Computation Efficiency of Visual Perception Streaming via Law-of-Charge-Conservation Inspired Spiking Neural Network (**CVPR 2025**). [[paper](https://cvpr.thecvf.com/virtual/2025/poster/34908)]

- Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement (**CVPR 2025**). [[paper](https://arxiv.org/abs/2503.16572)]

- Spiking Transformer: Introducing Accurate Addition-Only Spiking Self-Attention for Transformer (**CVPR 2025**). [[paper](https://arxiv.org/abs/2503.00226)]

- Brain-Inspired Spiking Neural Networks for Energy-Efficient Object Detection (**CVPR 2025**). [[paper](https://cvpr.thecvf.com/virtual/2025/poster/33275)]

- Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks (**CVPR 2025**). [[paper](https://arxiv.org/abs/2503.03144)]

- STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks (**CVPR 2025**). [[paper](https://arxiv.org/abs/2503.02689)]

- Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients (**CVPR 2025**). [[paper](https://arxiv.org/abs/2503.03272)]

- Rethinking Spiking Self-Attention Mechanism: Implementing α-XNOR Similarity Calculation in Spiking Transformers (**CVPR 2025**). [[paper](https://cvpr.thecvf.com/virtual/2025/poster/33850)]

- Spiking Transformer with Spatial-Temporal Attention (**CVPR 2025**). [[paper](https://arxiv.org/abs/2409.19764)]

- Quantized Spike-driven Transformer (**ICLR 2025**). [[paper](https://openreview.net/forum?id=5J9B7Sb8rO)]

- Rethinking Spiking Neural Networks from an Ensemble Learning Perspective (**ICLR 2025**). [[paper](https://openreview.net/forum?id=ZyknpOQwkT)]

- DeepTAGE: Deep Temporal-Aligned Gradient Enhancement for Optimizing Spiking Neural Networks (**ICLR 2025**). [[paper](https://openreview.net/forum?id=drPDukdY3t)]

- QP-SNN: Quantized and Pruned Spiking Neural Networks (**ICLR 2025**). [[paper](https://openreview.net/forum?id=MiPyle6Jef)]

- Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness (**ICLR 2025**). [[paper](https://openreview.net/forum?id=9HsfTgflT7)]

- P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks (**ICLR 2025**). [[paper](https://openreview.net/forum?id=Sf4ep9Udjf)]

- TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting (**ICLR 2025**). [[paper](https://openreview.net/forum?id=rDe9yQQYKt)]

- Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency (**ICLR 2025**). [[paper](https://openreview.net/forum?id=gcouwCx7dG)]

- SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking (**ICLR 2025**). [[paper](https://openreview.net/forum?id=ZadnlOHsHv)]

- Spiking Vision Transformer with Saccadic Attention (**ICLR 2025**). [[paper](https://openreview.net/forum?id=qzZsz6MuEq)]

- SpikeGS: Reconstruct 3D scene captured by a fast moving bio-inspired camera (**AAAI 2025**). [[paper](https://arxiv.org/pdf/2407.03771v2)]

- Rethinking High-speed Image Reconstruction Framework with Spike Camera (**AAAI 2025**). [[paper](https://arxiv.org/pdf/2501.04477)] [[code](https://github.com/chenkang455/SpikeCLIP)]

- Spiking Point Transformer for Point Cloud Classification (**AAAI 2025**).

- Efficient 3D Recognition with Event-driven Spike Sparse Convolution (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2412.07360)] [[code](https://github.com/bollossom/e-3dsnn)]

- GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2404.15597)]

- EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2405.18880)]

- Leveraging Asynchronous Spiking Neural Networks for Ultra Efficient Event-Based Visual Processing (**AAAI 2025**).

- CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal Dynamics  (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2412.12525)] [[code](https://github.com/shen-aoyu/CREST/)]

- UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks (**AAAI 2025**).

- SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models (**AAAI 2025**). [[paper](https://arxiv.org/pdf/2408.14909)][[code](https://github.com/shenshuaijie/SDN

)]

- Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning (**AAAI 2025**). [[paper](https://arxiv.org/pdf/2405.13672)]

- SpikingYOLOX: Improved YOLOX Object Detection with Fast Fourier Convolution and Spiking Neural Networks(**AAAI 2025**).

- ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2412.12696)]

- Efficient Spike-driven Transformer For High-performance Image Segmentation (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2412.14587)] [[code](https://github.com/BICLab/Spike2Former)]

- Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism (**AAAI 2025**).

- Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks (**AAAI 2025**).

- Towards More Discriminative Feature Learning in SNNs with Temporal-Self-Erasing Supervision (**AAAI 2025**).

- FSTA-SNN: Frequency-based Spatial-Temporal Attention Module for Spiking Neural Networks (**AAAI 2025**).[[paper](https://arxiv.org/pdf/2501.14744)] [[code](https://github.com/yukairong/FSTA-SNN)]



### 2024



**Review**

- Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods (**Frontiers in Neuroscience 2024**). [[paper]](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1383844/full) [[arxiv](https://arxiv.org/abs/2405.04289v2)] 



**NeurIPS, ACM MM, ECCV, AAAI, ICLR, Frontiers in Neuroscience, CVPR, ICML, IJCAI**

- SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-SNN Conversion with Winner-Oriented Spike Shift for Softmax Operation (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=fs28jccJj5)]

- Spiking Graph Neural Network on Riemannian Manifolds (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=VKt0K3iOmO)]

- Rethinking the Dynamics of Spiking Neural Networks (**NeurIPS 2024**). [[paper](https://neurips.cc/virtual/2024/poster/96543)]

- Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie Stimuli (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=bxDok3uaK6)] [[code](https://github.com/Grasshlw/SNN-Neural-Similarity-Movie)]

- Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=xjyU6zmZD7)]

- Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation (**NeurIPS 2024**). [[paper](https://arxiv.org/abs/2410.11488)] [[code](https://github.com/Tab-ct/rate-based-backpropagation)]

- Latent Diffusion for Neural Spiking Data (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=ZX6CEo1Wtv)]

- Autonomous Driving with Spiking Neural Networks (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=95VyH4VxN9)] [[code](https://github.com/ridgerchu/SAD)]

- Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals  (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=mCWZj7pa0M)]

- Spatio-Temporal Interactive Learning for Efficient Image Reconstruction of Spiking Cameras  (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=S4ZqnMywcM)]

- Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=4A5IQEjG8c)]

- EnOF: Training Accurate Spiking Neural Networks via Enhancing the Output Feature Representation (**NeurIPS 2024**). [[paper](https://openreview.net/pdf/5a4dfaf8dc6861efa8e8356b3bd86743ab98838d.pdf)]

- Spiking Token Mixer: A event-driven friendly Former structure for spiking neural networks (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=iYcY7KAkSy)] [[code](https://github.com/brain-intelligence-lab/STMixer_demo)]

- SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network (**NeurIPS 2024**). [[paper](https://arxiv.org/abs/2405.14398)] [[code](https://github.com/guoweiyu/SpGesture/)]

- Spiking Transformer with Experts Mixture (**NeurIPS 2024**). [[paper](https://openreview.net/pdf/35a5bc54de368426f66605d8e3f447638863888a.pdf)] 

- FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=TuCQdBo4NC)] [[code](https://github.com/zju-bmi-lab/FEEL_SNN)]

- Spiking Neural Network as Adaptive Event Stream Slicer (**NeurIPS 2024**). [[paper](https://arxiv.org/abs/2410.02249)] 

- Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators (**NeurIPS 2024**). [[paper](https://arxiv.org/abs/2405.14362)] [[code](https://github.com/microsoft/SeqSNN)]

- QKFormer: Hierarchical Spiking Transformer using Q-K Attention (**NeurIPS 2024**). [[paper](https://openreview.net/pdf?id=AVd7DpiooC)] [[code](https://github.com/zhouchenlin2096/QKFormer)]

- Q-SNNs: Quantized Spiking Neural Networks (**ACM MM 2024**). [[paper](https://dl.acm.org/doi/10.1145/3664647.3681186)]

- RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding (**ACM MM 2024**). [[paper](https://dl.acm.org/doi/10.1145/3664647.3680639)] [[code](https://github.com/KemingWu/RSC-SNN)]

- Reversing Structural Pattern Learning with Biologically Inspired Knowledge Distillation for Spiking Neural Networks (**ACM MM 2024**). [[paper](https://dl.acm.org/doi/pdf/10.1145/3664647.3680655)]

- Towards High-performance Spiking Transformers from ANN to SNN Conversion (**ACM MM 2024**). [[paper](https://dl.acm.org/doi/10.1145/3664647.3680620)]  [[code](https://github.com/h-z-h-cell/Transformer-to-SNN-ECMT)]

- Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks (**ACM MM 2024**). [[paper](https://dl.acm.org/doi/10.1145/3664647.3680832)]  [[code](https://github.com/hsw0929/HSD)]

- Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection (**ECCV 2024**). [[paper](https://arxiv.org/pdf/2407.20708)] [[code](https://github.com/BICLab/SpikeYOLO)]

- Spiking Wavelet Transformer (**ECCV 2024**). [[paper](https://arxiv.org/pdf/2403.11138)] [[code](https://github.com/bic-L/Spiking-Wavelet-Transformer)]

- Efficient Training of Spiking Neural Networks with Multi-Parallel Implicit Stream Architecture (**ECCV 2024**). [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/05068.pdf)] [[code](https://github.com/kiritozc/MPIS-SNNs)]

- Asynchronous Bioplausible Neuron for Spiking Neural Networks for Event-Based Vision (**ECCV 2024**). [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/08133.pdf)] 

- BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation (**ECCV 2024**). [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06649.pdf)] [[code](https://github.com/Intelligent-Computing-Research-Group/BKDSNN)]

- Exploring Vulnerabilities in Spiking Neural Networks: Direct Adversarial Attacks on Raw Event Data (**ECCV 2024**). [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09164.pdf)]

- EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks (**ECCV 2024**). [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07766.pdf)] [[code](https://github.com/Windere/EAS-SNN)]

- Spike-Temporal Latent Representation for Energy-Efficient Event-to-Video Reconstruction (**ECCV 2024**). [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/05843.pdf)]

- EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices (**IJCAI 2024**). [[code](https://github.com/AmazingDD/EC-SNN)] 

- One-step Spiking Transformer with a Linear Complexity (**IJCAI 2024**).

- TIM: An Efficient Temporal Interaction Module for Spiking Transformer (**IJCAI 2024**). [[paper](https://arxiv.org/pdf/2401.11687)] [[code](https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/TIM)] 

- Learning a Spiking Neural Network for Efficient Image Deraining (**IJCAI 2024**). [[code](https://github.com/MingTian99/ESDNet)] 

- LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization (**IJCAI 2024**). [[paper](https://arxiv.org/pdf/2401.14652)] 

- Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/35073)] 

- Towards efficient deep spiking neural networks construction with spiking activity based pruning (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/33505)] 

- Efficient and Effective Time-Series Forecasting with Spiking Neural Networks (**ICML 2024**). [[paper](https://arxiv.org/pdf/2402.01533)] 

- Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/33269)] 

- Robust Stable Spiking Neural Networks (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/33217)]

- CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks (**ICML 2024**). [[paper](https://arxiv.org/pdf/2402.04663)]

- NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks  (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/33481)]

- High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost (**ICML 2024**). [[paper](https://arxiv.org/pdf/2405.16466)]

- Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/32674)]

- SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/35024)]

- Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/33242)]

- Enhancing Adversarial Robustness in SNNs with Sparse Gradients (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/34066)]

- SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN (**ICML 2024**). [[paper](https://icml.cc/virtual/2024/poster/34194)]

- Are Conventional SNNs Really Efficient? A Perspective from Network Quantization  (**CVPR 2024**). [[paper](https://arxiv.org/pdf/2311.10802)]

- SFOD: Spiking Fusion Object Detector (**CVPR 2024**). [[paper](https://arxiv.org/pdf/2403.15192)] [[code](https://github.com/yimeng-fan/SFOD)]

- SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks (**CVPR 2024**). [[paper](https://arxiv.org/abs/2403.14302)] [[code](https://github.com/xyshi2000/SpikingResformer)]

- SGLFormer: Spiking Global-Local-Fusion Transformer with high performance (**Frontiers in Neuroscience 2024**).[[paper](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1371290/full)] [[code](https://github.com/ZhangHanN1/SGLFormer)]

- Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework  (**ICLR 2024**). [[paper](https://openreview.net/forum?id=eoSeaK4QJo&referrer=%5Bthe%20profile%20of%20Zecheng%20Hao%5D(%2Fprofile%3Fid%3D~Zecheng_Hao1))]

- Online Stabilization of Spiking Neural Networks  (**ICLR 2024**). [[paper](https://openreview.net/forum?id=CIj1CVbkpr)]

- SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition (**ICLR 2024**). [[paper](https://arxiv.org/pdf/2310.07189.pdf)]

- Spatio-Temporal Approximation: A Training-Free SNN Conversion for Transformers  (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=XrunSYwoLr)]

- Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=0jsfesDZDq)]

- Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=4r2ybzJnmN)] [[code](https://github.com/Thvnvtos/SNN-delays)]

- Threaten Spiking Neural Networks through Combining Rate and Temporal Information (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=xv8iGxENyI)] [[code](https://github.com/hzc1208/HART_Attack)]

- TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks (**ICLR 2024**). [[paper](https://openreview.net/forum?id=k1wlmtPGLq&noteId=p5M9gOLAOf)] 

- Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks (**ICLR 2024**). [[paper](https://openreview.net/forum?id=5bNYf0CqxY)] 

- Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=ZYm1Ql6udy)] 

- Adaptive deep spiking neural network with global-local learning via balanced excitatory and inhibitory mechanism (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=wpnlc2ONu0)] 

- Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks (**ICLR 2024**). [[paper](https://arxiv.org/pdf/2402.11984.pdf)] [[code](https://github.com/pkuxmq/HLOP-SNN)]

- A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=g52tgL8jy6)] [[code](https://github.com/hzc1208/STBP_LMH)]

- LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units (**ICLR 2024**). [[paper](https://arxiv.org/pdf/2402.04882.pdf)] [[code](https://github.com/zeyuliu1037/LMUFormer)]

- Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=1SIBN5Xyw7)] [[code](https://github.com/BICLab/Spike-Driven-Transformer-V2)]

- Can we get the best of both Binary Neural Networks and Spiking Neural Networks for Efficient Computer Vision? (**ICLR 2024**). [[paper](https://openreview.net/pdf?id=lGUyAuuTYZ)] [[code](https://github.com/godatta/Ultra-Low-Latency-SNN)]

- A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks (**ICLR 2024**).  [[paper](https://openreview.net/pdf?id=LnLySuf1vp)] [[code](https://github.com/EdisonLeeeee/SpikeGCL)]

- Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2312.06372.pdf)] [[code](https://github.com/yfguo91/Ternary-Spike)]

- Memory-Efficient Reversible Spiking Neural Networks (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2312.07922.pdf)] [[code](https://github.com/mi804/RevSNN)]

- Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2308.06582.pdf)]

- SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2308.10873.pdf)] [[code](https://github.com/NeuroCompLab-psu/SpikingBERT)]

- TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2308.13250.pdf)] [[code](https://github.com/ZhangShimin1/TC-LIF)]

- Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Networks (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2401.01912.pdf)]

- Dynamic Spiking Graph Neural Networks (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2401.05373.pdf)]

- An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain (**AAAI 2024**).  [[paper](https://arxiv.org/pdf/2303.13077.pdf)] [[code](https://github.com/Brain-Cog-Lab/Transfer-for-DVS)]



**Arxiv**

- Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities. [paper](https://doi.org/10.48550/arXiv.2401.02429)  

- Q-SNNs: Quantized Spiking Neural Networks. [[paper](https://arxiv.org/pdf/2406.13672)]

- Scalable MatMul-free Language Modeling. [[paper](https://arxiv.org/pdf/2406.02528)] [[code](https://github.com/ridgerchu/matmulfreellm)]

- QKFormer: Hierarchical Spiking Transformer using Q-K Attention. [[paper](https://arxiv.org/pdf/2403.16552.pdf)] [[code](https://github.com/zhouchenlin2096/QKFormer)]

- Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket. [[paper](https://arxiv.org/pdf/2401.02020.pdf)] [[code](https://github.com/ZK-Zhou/spikformer)]

- SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network Systems. [[paper](https://arxiv.org/pdf/2402.11322.pdf)]

- Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling. [[paper](https://arxiv.org/pdf/2312.07625v2.pdf)]



### 2023



**Review**

- Direct Learning-Based Deep Spiking Neural Networks: A Review (**Frontiers in Neuroscience 2023**). [[paper](https://arxiv.org/pdf/2305.19725.pdf)]



**AAAI, ICLR, CVPR, ICML, IJCAI, ICCV, NeurIPS, TPAMI, Science Advances**

- Time series prediction and anomaly detection with recurrent spiking neural networks (**IJCNN 2023**). [[paper](https://ieeexplore.ieee.org/abstract/document/10191614)]

- SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence (**Science Advances 2023**). [[paper](https://www.science.org/doi/10.1126/sciadv.adi1480)] [[code](https://github.com/fangwei123456/spikingjelly)]

- Spike-driven Transformer [[paper](https://arxiv.org/pdf/2307.01694.pdf)] [[code](https://github.com/BICLab/Spike-Driven-Transformer)]

- Parallel Spiking Neurons with High Efficiency and Long-term Dependencies Learning Ability (**NeurIPS 2023**). [[paper](https://arxiv.org/abs/2304.12760)] [[code](https://github.com/fangwei123456/Parallel-Spiking-Neuron)]

- Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes (**NeurIPS 2023**). [[paper](https://arxiv.org/pdf/2306.12045.pdf)]

- SEENN: Towards Temporal Spiking Early Exit Neural Networks (**NeurIPS 2023**). [[paper](https://openreview.net/pdf?id=mbaN0Y0QTw)]

- EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks (**NeurIPS 2023**). [[paper](https://openreview.net/pdf?id=OMDgOjdqoZ)]

- Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons (**NeurIPS 2023**). [[paper](https://openreview.net/pdf?id=Ht79ZTVMsn)]

- Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks (**NeurIPS 2023**). [[paper](https://openreview.net/pdf?id=aGZp61S9Lj)]

- Trial matching: capturing variability with data-constrained spiking neural networks (**NeurIPS 2023**). [[paper](https://arxiv.org/abs/2306.03603)]

- Evolving Connectivity for Recurrent Spiking Neural Networks (**NeurIPS 2023**). [[paper](https://arxiv.org/pdf/2305.17650.pdf)]

- SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks (**NeurIPS 2023**). [[paper](https://openreview.net/pdf?id=yzZbwQPkmP)] 

- Spiking PointNet: Spiking Neural Networks for Point Clouds (**NeurIPS 2023**). [[paper](https://arxiv.org/pdf/2310.06232v1.pdf)] [[code](https://github.com/dayongren/spiking-pointnet)]

- Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks (**NeurIPS 2023**). [[paper](https://openreview.net/pdf?id=8IvW2k5VeA)]

- Membrane Potential Batch Normalization for Spiking Neural Networks (**ICCV 2023**). [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Membrane_Potential_Batch_Normalization_for_Spiking_Neural_Networks_ICCV_2023_paper.pdf)]

- Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence (**ICCV 2023**). [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Li_Unleashing_the_Potential_of_Spiking_Neural_Networks_with_Dynamic_Confidence_ICCV_2023_paper.pdf)]

- RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks	(**ICCV 2023**). [[paper](https://arxiv.org/abs/2308.06787)]

- Inherent Redundancy in Spiking Neural Networks	(**ICCV 2023**). [[paper](https://arxiv.org/abs/2308.08227)]

- Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation (**ICCV 2023**). [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Wei_Temporal-Coded_Spiking_Neural_Networks_with_Dynamic_Firing_Threshold_Learning_with_ICCV_2023_paper.pdf)]

- Efficient Converted Spiking Neural Network for 3D and 2D Classification	(**ICCV 2023**). [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Lan_Efficient_Converted_Spiking_Neural_Network_for_3D_and_2D_Classification_ICCV_2023_paper.pdf)]

- Deep Directly-Trained Spiking Neural Networks for Object Detection (**ICCV 2023**). [[paper](https://arxiv.org/abs/2307.11411)]

- Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks (**ICCV 2023**). [[paper](https://arxiv.org/abs/2302.14311)]

- SSF: Accelerating Training of Spiking Neural Networks with Stabilized Spiking Flow (**ICCV 2023**). [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_SSF_Accelerating_Training_of_Spiking_Neural_Networks_with_Stabilized_Spiking_ICCV_2023_paper.pdf)]

- Masked Spiking Transformer (**ICCV 2023**). [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_Masked_Spiking_Transformer_ICCV_2023_paper.pdf)]

- Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks (**IJCAI 2023**). [[paper](https://www.ijcai.org/proceedings/2023/0344.pdf)]

- Learnable Surrogate Gradient for Direct Training Spiking Neural Networks (**IJCAI 2023**). [[paper](https://www.ijcai.org/proceedings/2023/0335.pdf)]

- Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks (**IJCAI 2023**). [[paper](https://www.ijcai.org/proceedings/2023/0334.pdf)]

- Adaptive Smoothing Gradient Learning for Spiking Neural Networks (**ICML 2023**). [[paper](https://openreview.net/pdf?id=GdkwSGTpbC)]

- Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks (**ICML 2023**). [[paper](https://openreview.net/pdf?id=zRkz4duLKp)] [[code](https://github.com/brain-intelligence-lab/surrogate_module_learning)]

- Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks (**CVPR 2023**). [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Bu_Rate_Gradient_Approximation_Attack_Threats_Deep_Spiking_Neural_Networks_CVPR_2023_paper.pdf)]

- Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation (**CVPR 2023**). [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Constructing_Deep_Spiking_Neural_Networks_From_Artificial_Neural_Networks_With_CVPR_2023_paper.pdf)]

- Attention Spiking Neural Networks  (**TPAMI 2023**) .[[paper](https://ieeexplore.ieee.org/abstract/document/10032591)] [[code](https://github.com/fangwei123456/spikingjelly/pull/329)]

- Heterogeneous neuronal and synaptic dynamics for spike-efficient unsupervised learning: Theory and design principles (**ICLR 2023**).[[paper](https://arxiv.org/pdf/2302.11618.pdf)]

- Spiking Convolutional Neural Networks for Text Classification (**ICLR 2023**) .[[paper](https://openreview.net/pdf?id=pgU3k7QXuz0)]

- Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes (**ICLR 2023**).[[paper](https://arxiv.org/pdf/2302.10685.pdf)] [[code](https://github.com/hzc1208/ANN2SNN_COS)]

- Spikformer: When Spiking Neural Network Meets Transformer (**ICLR 2023**) .[[paper](https://openreview.net/forum?id=frE4fUwz_h)] [[code](https://github.com/ZK-Zhou/spikformer)]

- A Unified Framework of Soft Threshold Pruning (**ICLR 2023**). [[paper](https://openreview.net/forum?id=cCFqcrq0d8)] [[code](https://github.com/Yanqi-Chen/LATS)]

- Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes (**ICLR 2023**). [[paper](https://openreview.net/forum?id=PFbzoWZyZRX)] [[code](https://github.com/hzc1208/ANN2SNN_COS)]

- Reducing ANN-SNN Conversion Error through Residual Membrane Potential (**AAAI 2023**). [[paper](https://arxiv.org/abs/2302.02091)] [[code](https://github.com/hzc1208/ANN2SNN_SRP)]

- Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse (**AAAI 2023**). [[paper](https://arxiv.org/abs/2303.06060)]

- ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks (**AAAI 2023**). [[paper](https://arxiv.org/pdf/2306.03693.pdf)]

- Exploring Temporal Information Dynamics in Spiking Neural Networks (**AAAI 2023**). [[paper](https://arxiv.org/pdf/2211.14406.pdf)] [[code](https://github.com/Intelligent-Computing-Lab-Yale/Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks)]

- Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks(**AAAI 2023**). [[paper](https://arxiv.org/pdf/2208.10364.pdf)] [[code](https://github.com/EdisonLeeeee/SpikeNet)]

- Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition(**AAAI 2023**). [[paper](https://arxiv.org/pdf/2302.01194.pdf)] 



**Arxiv**

- Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network [[paper](https://arxiv.org/abs/2304.11954)] [[code](https://github.com/zhouchenlin2096/Spikingformer)]

- Enhancing the Performance of Transformer-based Spiking Neural Networks by Improved Downsampling with Precise Gradient Backpropagation [[paper](https://arxiv.org/abs/2305.05954)] [[code](https://github.com/zhouchenlin2096/Spikingformer-CML)]

- Training Full Spike Neural Networks via Auxiliary Accumulation Pathway [[paper](https://arxiv.org/pdf/2301.11929.pdf)]

- MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network [[paper](https://arxiv.org/abs/2211.12156)]

- SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks [[paper](https://arxiv.org/abs/2302.13939)] [[code](https://github.com/ridgerchu/SpikeGPT)]

- Auto-Spikformer: Spikformer Architecture Search [[paper](https://arxiv.org/pdf/2306.00807.pdf)]

- Advancing Spiking Neural Networks Towards Deep Residual Learning [[paper](https://arxiv.org/pdf/2112.08954.pdf)]



### 2022



**NeurIPS, CVPR, ICLR, AAAI, ICML, Nature Communications**



- Event-based Video Reconstruction via Potential-assisted Spiking Neural Network [[paper](https://arxiv.org/abs/2201.10943)] [[code](https://github.com/LinZhu111/EVSNN)]

- Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks [[paper](https://openreview.net/forum?id=7B3IJMM1k_M)] [[code](https://github.com/putshua/SNN-conversion-QCFS)]

- Optimized Potential Initialization for Low-latency Spiking Neural Networks (**AAAI 2022**).  [[paper](https://arxiv.org/abs/2202.01440)]

- AutoSNN: Towards Energy-Efficient Spiking Neural Networks [[paper](https://arxiv.org/abs/2201.12738)]

- Neural Architecture Search for Spiking Neural Networks [[paper](https://arxiv.org/abs/2201.10355)] [[code](https://github.com/Intelligent-Computing-Lab-Yale/Neural-Architecture-Search-for-Spiking-Neural-Networks)]

- Neuromorphic Data Augmentation for Training Spiking Neural Networks [[paper](https://arxiv.org/abs/2203.06145)] [[code](https://github.com/Intelligent-Computing-Lab-Yale/NDA_SNN)]

- State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks [[paper](https://proceedings.mlr.press/v162/chen22ac.html)] [[code](https://github.com/Yanqi-Chen/STDS)]

- Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [[paper](https://arxiv.org/abs/2205.00459)] [[code](https://github.com/qymeng94/DSR)]

- Exploring Lottery Ticket Hypothesis in Spiking Neural Networks [[paper](https://arxiv.org/abs/2207.01382)] [[code](https://github.com/Intelligent-Computing-Lab-Yale/Exploring-Lottery-Ticket-Hypothesis-in-SNNs)]

- Spiking Graph Convolutional Networks [[paper](https://arxiv.org/abs/2205.02767)] [[code](https://github.com/ZulunZhu/SpikingGCN)]

- A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system [[paper](https://www.nature.com/articles/s41467-022-31747-w)] [[code](https://github.com/billyuanpku96/SNN-for-sensory-neuron)]

- Online Training Through Time for Spiking Neural Networks (**NeurIPS 2022**).  [[paper](https://arxiv.org/abs/2210.04195)] [[code](https://github.com/pkuxmq/OTTT-SNN)]

- Training Spiking Neural Networks with Event-driven Backpropagation [[paper](https://openreview.net/forum?id=d4JmP1T45WE)] [[code](https://github.com/zhuyaoyu/SNN-event-driven-learning)]

- GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks [[paper](https://openreview.net/forum?id=UmFSx2c4ubT)] [[code](https://github.com/Ikarosy/Gated-LIF)]

- Temporal Effective Batch Normalization in Spiking Neural Networks [[paper](https://openreview.net/forum?id=fLIgyyQiJqz)]

- Training Spiking Neural Networks with Local Tandem Learning (**NeurIPS 2022**). [[paper](https://arxiv.org/pdf/2210.04532.pdf)]

- IM-Loss: Information Maximization Loss for Spiking Neural Networks (**NeurIPS 2022**). [[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/010c5ba0cafc743fece8be02e7adb8dd-Paper-Conference.pdf)]

- Temporal Effective Batch Normalization in Spiking Neural Networks (**NeurIPS 2022**). [[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/de2ad3ed44ee4e675b3be42aa0b615d0-Paper-Conference.pdf)]

- Biologically Inspired Dynamic Thresholds for Spiking Neural Networks (**NeurIPS 2022**). [[paper](https://arxiv.org/pdf/2206.04426.pdf)]

- Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks (**ICLR 2022**).  [[paper](https://arxiv.org/pdf/2103.00476.pdf)] [[code](https://github.com/Jackn0/snn_optimal_conversion_pipeline)]

- Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks (**IJCAI 2022**). [[paper](https://arxiv.org/pdf/2210.06386.pdf)]



### 2021



**NeurIPS, ICCV, IJCAI, ICML, AAAI**



- Deep Residual Learning in Spiking Neural Networks (**NeurIPS 2021**). [[paper](https://proceedings.neurips.cc/paper/2021/file/afe434653a898da20044041262b3ac74-Paper.pdf)] [[code](https://github.com/fangwei123456/Spike-Element-Wise-ResNet)]

- Spiking Deep Residual Network[[paper](https://arxiv.org/pdf/1805.01352.pdf)]

- Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks (**ECCV 2021**).  [[paper](https://arxiv.org/abs/2007.05785)]  [[code](https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron)]

- Pruning of Deep Spiking Neural Networks through Gradient Rewiring [[paper](https://arxiv.org/abs/2105.04916)] [[code](https://github.com/Yanqi-Chen/Gradient-Rewiring)]

- A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration  (**ICML 2021**).  [[paper](https://arxiv.org/pdf/2106.06984)] [[code](https://github.com/yhhhli/SNN_Calibration)]

- Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks [[paper](https://arxiv.org/pdf/2105.11654)] [[code](https://github.com/DingJianhao/OptSNNConvertion-RNL-RIL)]

- Sparse Spiking Gradient Descent (**NeurIPS 2021**). [[paper](https://proceedings.neurips.cc/paper/2021/file/61f2585b0ebcf1f532c4d1ec9a7d51aa-Paper.pdf)]

- Training Spiking Neural Networks with Accumulated Spiking Flow (**AAAI 2021**). [[paper](https://arxiv.org/pdf/2011.05280.pdf)]

- Temporal-wise Attention Spiking Neural Networks for Event Streams Classification. (**ECCV 2021**). [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Yao_Temporal-Wise_Attention_Spiking_Neural_Networks_for_Event_Streams_Classification_ICCV_2021_paper.pdf)]



### Reference

If you find this repo useful, please consider citing:

```

@article{zhou2024direct,

  title={Direct training high-performance deep spiking neural networks: a review of theories and methods},

  author={Zhou, Chenlin and Zhang, Han and Yu, Liutao and Ye, Yumin and Zhou, Zhaokun and Huang, Liwei and Ma, Zhengyu and Fan, Xiaopeng and Zhou, Huihui and Tian, Yonghong},

  journal={Frontiers in Neuroscience},

  volume={18},

  pages={1383844},

  year={2024},

  publisher={Frontiers Media SA}

}

```