  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.
