Spiking Neural Networks (SNNs) promise efficient spatio-temporal data processing owing to their dynamic nature. This paper addresses a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, by reformulating it as a time-series segmentation task inherently suited for SNN execution. Automated RFI detection systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. We explore several spectrogram-to-spike encoding methods and network parameters, applying first-order leaky integrate-and-fire SNNs to tackle RFI detection. To enhance the contrast between RFI and background information, we introduce a divisive normalisation-inspired pre-processing step, which improves detection performance across multiple encoding strategies. Our approach achieves competitive performance on a synthetic dataset and compelling results on real data from the Low-Frequency Array (LOFAR) instrument. To our knowledge, this work is the first to train SNNs on real radio astronomy data successfully. These findings highlight the potential of SNNs for performing complex time-series tasks, paving the way for efficient, real-time processing in radio astronomy and other data-intensive fields.

翻译：脉冲神经网络（SNNs）因其动态特性，在时空数据处理方面展现出高效潜力。本文通过将射电天文中的一项重要挑战——射频干扰（RFI）检测——重新定义为本质上适合SNN执行的时序分割任务，来解决该问题。能够在低能耗下实时运行的自动化RFI检测系统在现代射电望远镜中日益重要。我们探索了多种频谱图到脉冲的编码方法和网络参数，并应用一阶泄漏积分发放脉冲神经网络来处理RFI检测任务。为增强RFI与背景信息之间的对比度，我们引入了一种受除法归一化启发的预处理步骤，该步骤在多种编码策略下均提升了检测性能。我们的方法在合成数据集上取得了具有竞争力的性能，并在来自低频阵列（LOFAR）设备的真实数据上获得了令人信服的结果。据我们所知，本研究是首次成功在真实射电天文数据上训练SNNs的工作。这些发现凸显了SNNs在执行复杂时序任务方面的潜力，为射电天文及其他数据密集型领域实现高效实时处理开辟了道路。