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 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.

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Lecture Notes in Computer Science
Perceptive acting under uncertainty:\r\nsafety solutions for autonomous systems , Human Brain Project - SGA3 , Efficient Deep Learning Platforms
32nd International Conference on Artificial Neural Networks, ICANN 2023

Sun, T., Yin, B., & Bohte, S. (2023). Efficient uncertainty estimation in Spiking Neural Networks via MC-dropout. In Proceedings of the International Conference of Artificial Neural Networks (pp. 393–406). doi:10.1007/978-3-031-44207-0_33