mlsanityData to 7 Oct 2026

Spiking Neural Networks for Frame-based and Event-based Single Object Localization

arXiv 2206.06506 ↗First posted 13 Jun 2022cs.CVcited by 18

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Abstract (arXiv)

Spiking neural networks have shown much promise as an energy-efficient alternative to artificial neural networks. However, understanding the impacts of sensor noises and input encodings on the network activity and performance remains difficult with common neuromorphic vision baselines like classification. Therefore, we propose a spiking neural network approach for single object localization trained using surrogate gradient descent, for frame- and event-based sensors. We compare our method with similar artificial neural networks and show that our model has competitive/better performance in accuracy, robustness against various corruptions, and has lower energy consumption. Moreover, we study the impact of neural coding schemes for static images in accuracy, robustness, and energy efficiency. Our observations differ importantly from previous studies on bio-plausible learning rules, which helps in the design of surrogate gradient trained architectures, and offers insight to design priorities in future neuromorphic technologies in terms of noise characteristics and data encoding methods.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 40 of its 91 references that are in the map. First posted on arXiv 13 Jun 2022.

Builds on

40 of 91 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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  5. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  6. 2019Event-based Vision: A Surveycited by 1,010
  7. 2015Cyclical Learning Rates for Training Neural Networkscited by 627
  8. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  9. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  10. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
  11. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  12. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
28 more
  1. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  2. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  3. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  4. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  5. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 222
  6. 2016UnitBox: An Advanced Object Detection Networkcited by 205
  7. 2020Learning to Detect Objects with a 1 Megapixel Event Cameracited by 184
  8. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  9. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 141
  10. 2015Spiking Deep Networks with LIF Neuronscited by 63
  11. 2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 54
  12. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  13. 2019Recent Advances in Deep Learning for Object Detectioncited by 50
  14. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 47
  15. 2020Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applicationscited by 34
  16. 2021StereoSpike: Depth Learning with a Spiking Neural Networkcited by 21
  17. 2020Low-activity supervised convolutional spiking neural networks applied to speech commands recognitioncited by 19
  18. 2021The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardwarecited by 19
  19. 2018Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domaincited by 15
  20. 2020Towards Understanding the Effect of Leak in Spiking Neural Networkscited by 15
  21. 2021Unraveling the paradox of intensity-dependent DVS pixel noisecited by 15
  22. 2019Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDPcited by 14
  23. 2016Training Spiking Deep Networks for Neuromorphic Hardwarecited by 12
  24. 2019Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?cited by 12
  25. 2019On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processorcited by 8
  26. 2021A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDPcited by 4
  27. 2021Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learningcited by 3
  28. 2018Object Detection based on LIDAR Temporal Pulses using Spiking Neural Networkscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 7; 2025: 3; 2026: 2.

18 papers.

Sort:

  1. 2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 163
  2. 2023SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networkscited by 82
  3. 2024EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splattingcited by 20
  4. 2024LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Unitscited by 11
  5. 2022Spiking neural networks for nonlinear regressioncited by 10
  6. 2023Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Camerascited by 10
  7. 2023To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Accelerationcited by 10
  8. 2024EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Visioncited by 8
  9. 2024Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAscited by 7
  10. 2025Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Applicationcited by 6
  11. 2024Neuromorphic Valence and Arousal Estimationcited by 5
  12. 2024SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networkscited by 2
6 more
  1. 2023Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discoverycited by 1
  2. 2024A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanismcited by 0
  3. 2025Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Mediacited by 0
  4. 2025Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithmscited by 0
  5. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0
  6. 2026Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNscited by 0