mlsanityData to 14 Oct 2026

RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks

arXiv 2308.06787 ↗First posted 13 Aug 2023cs.CVcited by 28

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Abstract (arXiv)

Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the real-valued membrane potentials to 0/1 spikes to transmit information thus the multiplications of activations and weights can be replaced by additions when implemented on hardware. However, this quantization mechanism will inevitably introduce quantization error, thus causing catastrophic information loss. To address the quantization error problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust the distribution which is directly related to quantization error to a range close to the spikes. Our method is extremely simple to implement and straightforward to train an SNN. Furthermore, it is shown to consistently outperform previous state-of-the-art methods over different network architectures and datasets.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 48 of its 70 references that are in the map. First posted on arXiv 13 Aug 2023.

Builds on

48 of 70 references.

References · 48

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  3. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  4. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  5. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  6. 2018AutoAugment: Learning Augmentation Policies from Datacited by 730
  7. 2017Channel Pruning for Accelerating Very Deep Neural Networkscited by 702
  8. 2021BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstructioncited by 479
  9. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  10. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  11. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  12. 2018Model compression via distillation and quantizationcited by 289
36 more
  1. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  2. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  3. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 266
  4. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  5. 2019Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networkscited by 238
  6. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 233
  7. 2016Training Deep Spiking Neural Networks using Backpropagationcited by 205
  8. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  9. 2017SuperSpike: Supervised learning in multi-layer spiking neural networkscited by 182
  10. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
  11. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 145
  12. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 144
  13. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 139
  14. 2018Activation Functions: Comparison of trends in Practice and Research for Deep Learningcited by 130
  15. 2019Enabling Spike-based Backpropagation for Training Deep Neural Network Architecturescited by 123
  16. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 122
  17. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 117
  18. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 100
  19. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  20. 2020DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networkscited by 71
  21. 2020LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processingcited by 56
  22. 2023Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillationcited by 44
  23. 2020Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networkscited by 43
  24. 2022Optimized Potential Initialization for Low-latency Spiking Neural Networkscited by 41
  25. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 40
  26. 2023Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikescited by 40
  27. 2016Neuromorphic Deep Learning Machinescited by 38
  28. 2023Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorizationcited by 35
  29. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  30. 2023ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networkscited by 23
  31. 2022Real Spike: Learning Real-valued Spikes for Spiking Neural Networkscited by 22
  32. 2019Spiking Neural Predictive Coding for Continual Learning from Data Streamscited by 17
  33. 2019Technical report: supervised training of convolutional spiking neural networks with PyTorchcited by 13
  34. 2023Biologically inspired structure learning with reverse knowledge distillation for spiking neural networkscited by 11
  35. 2022Navigating Local Minima in Quantized Spiking Neural Networkscited by 4
  36. 2023NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNNcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 14; 2026: 6.

28 papers.

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  1. 2023Spiking PointNet: Spiking Neural Networks for Point Cloudscited by 34
  2. 2024SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanismscited by 31
  3. 2024Learning A Spiking Neural Network for Efficient Image Derainingcited by 16
  4. 2025Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformercited by 13
  5. 2025Rethinking Spiking Neural Networks from an Ensemble Learning Perspectivecited by 12
  6. 2024LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Modelcited by 11
  7. 2025STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networkscited by 11
  8. 2024Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networkscited by 9
  9. 2025TS-SNN: Temporal Shift Module for Spiking Neural Networkscited by 5
  10. 2025Efficient Spiking Point Mamba for Point Cloud Analysiscited by 4
  11. 2024Temporal Reversal Regularization for Spiking Neural Networks: Hybrid Spatio-Temporal Invariance for Generalizationcited by 2
  12. 2025Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillerscited by 2
16 more
  1. 2025New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehiclescited by 2
  2. 2025Decision SpikeFormer: Spike-Driven Transformer for Decision Makingcited by 1
  3. 2026Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradientscited by 1
  4. 2026A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networkscited by 1
  5. 2023When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networkscited by 0
  6. 2024Deep activity propagation via weight initialization in spiking neural networkscited by 0
  7. 2025Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neuronscited by 0
  8. 2025Integer Binary-Range Alignment Neuron for Spiking Neural Networkscited by 0
  9. 2025SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learningcited by 0
  10. 2025Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mappingcited by 0
  11. 2025SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videoscited by 0
  12. 2025Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradientcited by 0
  13. 2026Learnable Burst Quantization for Expressive and Efficient Spiking Neural Networkscited by 0
  14. 2026ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantizationcited by 0
  15. 2026DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancementcited by 0
  16. 2026E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Camerascited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural Network
  2. 2023InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks
  3. 2026Reducing ANN-SNN Conversion Error via Residual Membrane Potential Alignment ↗
  4. 2024Q-SNNs: Quantized Spiking Neural Networks
  5. 2026PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks
  6. 2023MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
  7. 2021Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
  8. 2023Membrane Potential Distribution Adjustment and Parametric Surrogate Gradient in Spiking Neural Networks