mlsanityData to 7 Oct 2026

SLAYER: Spike Layer Error Reassignment in Time

arXiv 1810.08646 ↗First posted 5 Sep 2018cs.NE cs.LG stat.MLcited by 257

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Abstract (arXiv)

Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general backpropagation mechanism for learning synaptic weights and axonal delays which overcomes the problem of non-differentiability of the spike function and uses a temporal credit assignment policy for backpropagating error to preceding layers. We describe and release a GPU accelerated software implementation of our method which allows training both fully connected and convolutional neural network (CNN) architectures. Using our software, we compare our method against existing SNN based learning approaches and standard ANN to SNN conversion techniques and show that our method achieves state of the art performance for an SNN on the MNIST, NMNIST, DVS Gesture, and TIDIGITS datasets.

Key facts. Cited by 257 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2018.

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Citing papers per year: 2021: 37; 2022: 57; 2023: 58; 2024: 49; 2025: 36; 2026: 20.

257 papers.

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  1. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 263
  2. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  3. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  4. 2022Recurrent Vision Transformers for Object Detection with Event Camerascited by 158
  5. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 134
  6. 2022AEGNN: Asynchronous Event-based Graph Neural Networkscited by 118
  7. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 113
  8. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 111
  9. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
  10. 2022Attention Spiking Neural Networkscited 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. 2022Object Detection with Spiking Neural Networks on Automotive Event Datacited by 79
  2. 2021Time-Ordered Recent Event (TORE) Volumes for Event Camerascited by 73
  3. 2023GET: Group Event Transformer for Event-Based Visioncited by 73
  4. 2022Event-based Video Reconstruction via Potential-assisted Spiking Neural Networkcited by 58
  5. 2022Neural Architecture Search for Spiking Neural Networkscited by 52
  6. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  7. 2022Event Transformer. A sparse-aware solution for efficient event data processingcited by 48
  8. 2022Online Training Through Time for Spiking Neural Networkscited by 48
  9. 2023Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacingscited by 48
  10. 2024QKFormer: Hierarchical Spiking Transformer using Q-K Attentioncited by 46
  11. 2021Sparse Spiking Gradient Descentcited by 41
  12. 2023Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependenciescited by 41
  13. 2021Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium Statecited by 39
  14. 2022Optimized Potential Initialization for Low-latency Spiking Neural Networkscited by 39
  15. 2022RGB-Event Fusion for Moving Object Detection in Autonomous Drivingcited by 39
  16. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 39
  17. 2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 39
  18. 2023Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networkscited by 37
  19. 2022TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networkscited by 33
  20. 2024ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and Morecited by 33
  21. 2021A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardwarecited by 32
  22. 2023SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognitioncited by 30
  23. 2021SpikeMS: Deep Spiking Neural Network for Motion Segmentationcited by 29
  24. 2024Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticketcited by 28
  25. 2021CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processorcited by 27
  26. 2021Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learningcited by 27
  27. 2023RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networkscited by 27
  28. 2023Training Full Spike Neural Networks via Auxiliary Accumulation Pathwaycited by 26
  29. 2021Learning from Event Cameras with Sparse Spiking Convolutional Neural Networkscited by 25
  30. 2022Exploring Lottery Ticket Hypothesis in Spiking Neural Networkscited by 25
  31. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 22
  32. 2021Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Visioncited by 22
  33. 2022Real Spike: Learning Real-valued Spikes for Spiking Neural Networkscited by 22
  34. 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligencecited by 21
  35. 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
  36. 2022STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networkscited by 20
  37. 2021The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardwarecited by 19
  38. 2021One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latencycited by 17

50 most cited of 257 All 257 (JSON)