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Vmem-varphi: Low-Compute Out-of-Distribution Detection in Spiking Neural Networks from Membrane-Potential Statistics

arXiv 2610.00350 ↗First posted 29 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Spiking Neural Networks (SNNs) offer an energy-efficient approach to processing event-camera data, yet out-of-distribution (OOD) detection remains challenging in this setting. Existing OOD detection methods often depend on model outputs or computational components that are unavailable in object detection SNNs or are poorly suited to low-compute deployment. To that effect, we show that the subthreshold membrane potential (Vmem(t)) provides a useful internal signal for detecting distribution shifts. Simple per-channel statistics derived from these membrane dynamics enable OOD detection. To evaluate this approach, we introduce Gen1-C, an event-camera corruption benchmark developed upon the Prophesee Gen1 automotive detection dataset, containing six sensor-motivated histogram-level stress tests at five severity levels. We further propose the Multi-Descriptor Deviation (MDD), a corruption-blind method that operates on membrane-potential statistics. At the highest corruption severity, MDD achieves an AUROC of more than 0.88 on five of the six corruptions using only a bounded 64-frame observation window. Notably, the remaining corruption is also the one that has the smallest effect on the underlying detector. These results show that the temporal membrane-potential dynamics can provide an effective and low-cost signal for OOD detection in SNN-based event perception.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 29 Sep 2026.

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References · 20

  1. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  3. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  4. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  5. 2019Event-based Vision: A Surveycited by 1,010
  6. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  7. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  8. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  9. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 404
  10. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  11. 2016Revisiting Classifier Two-Sample Testscited by 258
  12. 2020Learning to Detect Objects with a 1 Megapixel Event Cameracited by 184
8 more
  1. 2020A Large Scale Event-based Detection Dataset for Automotivecited by 108
  2. 2021N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Camerascited by 91
  3. 2024Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detectioncited by 65
  4. 2020Event Camera Calibration of Per-pixel Biased Contrast Thresholdcited by 21
  5. 2024Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attentioncited by 5
  6. 2024Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flowscited by 4
  7. 2024Forward-Forward Learning achieves Highly Selective Latent Representations for Out-of-Distribution Detection in Fully Spiking Neural Networkscited by 3
  8. 2021Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embeddingcited by 2

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