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

arXiv: https://arxiv.org/abs/2206.06506 · HTML: https://mlsanity.com/paper/2206.06506/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 91 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Spiking Deep Networks with LIF Neurons](https://mlsanity.com/paper/1510.08829/) (2015)
- [Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/2110.07742/) (2021)
- [Neuromorphic Data Augmentation for Training Spiking Neural Networks](https://mlsanity.com/paper/2203.06145/) (2022)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations](https://mlsanity.com/paper/2003.10399/) (2020)
- [Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications](https://mlsanity.com/paper/2007.05657/) (2020)
- [StereoSpike: Depth Learning with a Spiking Neural Network](https://mlsanity.com/paper/2109.13751/) (2021)
- [Low-activity supervised convolutional spiking neural networks applied to speech commands recognition](https://mlsanity.com/paper/2011.06846/) (2020)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)

## Built on by (most cited first, 18 of 18)

- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks](https://mlsanity.com/paper/2302.13939/) (2023)
- [EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting](https://mlsanity.com/paper/2405.14959/) (2024)
- [LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units](https://mlsanity.com/paper/2402.04882/) (2024)
- [Spiking neural networks for nonlinear regression](https://mlsanity.com/paper/2210.03515/) (2022)
- [Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras](https://mlsanity.com/paper/2304.10211/) (2023)
- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision](https://mlsanity.com/paper/2412.07080/) (2024)
- [Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs](https://mlsanity.com/paper/2406.07318/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [Neuromorphic Valence and Arousal Estimation](https://mlsanity.com/paper/2401.16058/) (2024)
- [SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2404.19668/) (2024)
- [Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery](https://mlsanity.com/paper/2312.07466/) (2023)
- [A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism](https://mlsanity.com/paper/2409.12691/) (2024)
- [Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media](https://mlsanity.com/paper/2501.03874/) (2025)
- [Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms](https://mlsanity.com/paper/2505.16362/) (2025)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNs](https://mlsanity.com/paper/2608.29869/) (2026)

## 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.