mlsanityData to 7 Oct 2026

Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout

arXiv 2304.10191 ↗First posted 20 Apr 2023cs.NE cs.AI cs.CVcited by 5

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Abstract (arXiv)

Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware. As with classical artificial neural networks (ANNs), predictive uncertainties are important for decision making in high-stakes applications, such as autonomous vehicles, medical diagnosis, and high frequency trading. Yet, discussion of uncertainty estimation in SNNs is limited, and approaches for uncertainty estimation in artificial neural networks (ANNs) are not directly applicable to SNNs. Here, we propose an efficient Monte Carlo(MC)-dropout based approach for uncertainty estimation in SNNs. Our approach exploits the time-step mechanism of SNNs to enable MC-dropout in a computationally efficient manner, without introducing significant overheads during training and inference while demonstrating high accuracy and uncertainty quality.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 20 Apr 2023.

Builds on

13 of 28 references.

References · 13

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  5. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  6. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  7. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  8. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  9. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  10. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  11. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 113
  12. 2021Accurate online training of dynamical spiking neural networks through Forward Propagation Through Timecited by 33
1 more
  1. 2023Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.

5 papers.

  1. 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guaranteescited by 8
  2. 2024Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regressioncited by 1
  3. 2025On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysiscited by 1
  4. 2025A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classificationcited by 0
  5. 2026Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Trainingcited by 0