Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout

Understand

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.