mlsanityData to 14 Oct 2026

Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

arXiv 1706.02609 ↗First posted 8 Jun 2017cs.NE q-bio.NC stat.MLcited by 406

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Abstract (arXiv)

Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal information. Although pre-training from ANN or direct training based on backpropagation (BP) makes the supervised training of SNNs possible, these methods only exploit the networks' spatial domain information which leads to the performance bottleneck and requires many complicated training skills. Another fundamental issue is that the spike activity is naturally non-differentiable which causes great difficulties in training SNNs. To this end, we build an iterative LIF model that is more friendly for gradient descent training. By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology. We achieve the best performance of multi-layered perceptron (MLP) compared with existing state-of-the-art algorithms over the static MNIST and the dynamic N-MNIST dataset as well as a custom object detection dataset. This work provides a new perspective to explore the high-performance SNNs for future brain-like computing paradigm with rich spatio-temporal dynamics.

Key facts. Cited by 406 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2017.

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Citing papers per year: 2021: 39; 2022: 54; 2023: 86; 2024: 76; 2025: 92; 2026: 59.

406 papers.

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  1. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  2. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  3. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  4. 2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 169
  5. 2023Spike-driven Transformercited by 146
  6. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 139
  7. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 117
  8. 2022Attention Spiking Neural Networkscited by 107
  9. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
  10. 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chipscited by 103
  11. 2021Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networkscited by 99
  12. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
38 more
  1. 2023SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networkscited by 88
  2. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 79
  3. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
  4. 2021Time-Ordered Recent Event (TORE) Volumes for Event Camerascited by 73
  5. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 65
  6. 2022GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networkscited by 63
  7. 2022Event-based Video Reconstruction via Potential-assisted Spiking Neural Networkcited by 59
  8. 2024Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Trainingcited by 59
  9. 2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 55
  10. 2022Neural Architecture Search for Spiking Neural Networkscited by 53
  11. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  12. 2023Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipelinecited by 52
  13. 2022Event Transformer. A sparse-aware solution for efficient event data processingcited by 50
  14. 2022Online Training Through Time for Spiking Neural Networkscited by 49
  15. 2024QKFormer: Hierarchical Spiking Transformer using Q-K Attentioncited by 49
  16. 2023Hierarchical Neural Memory Network for Low Latency Event Processingcited by 47
  17. 2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNcited by 46
  18. 2023Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillationcited by 44
  19. 2021Pruning of Deep Spiking Neural Networks through Gradient Rewiringcited by 43
  20. 2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 41
  21. 2023Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependenciescited by 41
  22. 2021Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium Statecited by 40
  23. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 40
  24. 2023Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikescited by 40
  25. 2023Ternary Spike: Learning Ternary Spikes for Spiking Neural Networkscited by 38
  26. 2024OpenESS: Event-based Semantic Scene Understanding with Open Vocabulariescited by 38
  27. 2023Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networkscited by 37
  28. 2022TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networkscited by 35
  29. 2023Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorizationcited by 35
  30. 2023Membrane Potential Batch Normalization for Spiking Neural Networkscited by 35
  31. 2021A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detectioncited by 31
  32. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  33. 2024SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanismscited by 31
  34. 2024SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spikingcited by 31
  35. 2022Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikescited by 30
  36. 2024Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticketcited by 30
  37. 2023RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networkscited by 28
  38. 2021Visual Explanations from Spiking Neural Networks using Interspike Intervalscited by 27

50 most cited of 406 All 406 (JSON)