Uncertainty-aware spiking neural networks for regression

Uncertainty estimation is a key component for quantifying the reliability of modern deep learning models, and is crucial for many real-world applications. However, efficient methods for uncertainty estimation in spiking neural networks (SNNs), particularly for regression tasks, remain underexplored. In this work, we demonstrate that uncertainty estimation in SNNbased regression tasks can be effectively achieved using recent frameworks originally developed for classification. Specifically, we adapt these frameworks to regression with two uncertaintyaware approaches: (1) a heteroscedastic Gaussian method, in which the SNN predicts both the mean and variance of the target variable; and (2) a Regression-as-Classification (RAC) method, which reformulates regression as a classification task to enable probabilistic modeling. We evaluate our approaches on a toy dataset and several benchmark regression datasets, showing that these approaches deliver efficient and high-quality uncertainty estimates, comparable to or surpassing state-of-theart deep neural network baselines. Our findings underscore the potential of SNNs for uncertainty estimation in regression tasks, offering a biologically inspired and energy-efficient solution for applications requiring both accuracy and robustness, while also paving the way for broader adoption of SNNs in sequential and event-driven domains.

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