Capturing Uncertainty over Time for Spiking Neural Networks by Exploiting Conformal Prediction Sets

There is a great interest in harnessing the advantages of spiking neural networks. An increasing portion of research is focusing on the deployment of such models. The problem of safe decision making has similarities with classical networks. We apply spiking neural networks to time-series classification tasks where their stateful nature is beneficial. We show that the well-known method of Conformal Prediction (CP) is capable of distinguishing between wrong and correct decisions in this setting similar to but while being less expensive than Evidential Deep Learning and Neural Network Ensembles. In this work we argue that classification uncertainty in time should additionally be considered but is not captured by the length of prediction sets output from CP. Our main contribution addresses the issue that existing CP methods for classification do not consider the aforementioned problem. Our method takes as input the prediction sets which can be output from present conformal prediction and then extends these methods by a smoothed length and combined set algorithm. We apply our method to spiking neural network-based classifiers trained on four different time-series datasets. We show that our method outputs a more suitable uncertainty metric at a given point in time than just the unmodified set length of CP for classification.