  Detecting and mitigating Radio Frequency Interference (RFI) is critical for
enabling and maximising the scientific output of radio telescopes. The
emergence of machine learning methods has led to their application in radio
astronomy, and in RFI detection. Spiking Neural Networks (SNNs), inspired by
biological systems, are well-suited for processing spatio-temporal data. This
study introduces the first exploratory application of SNNs to an astronomical
data-processing task, specifically RFI detection. We adapt the
nearest-latent-neighbours (NLN) algorithm and auto-encoder architecture
proposed by previous authors to SNN execution by direct ANN2SNN conversion,
enabling simplified downstream RFI detection by sampling the naturally varying
latent space from the internal spiking neurons. Our subsequent evaluation aims
to determine whether SNNs are viable for future RFI detection schemes. We
evaluate detection performance with the simulated HERA telescope and
hand-labelled LOFAR observation dataset the original authors provided. We
additionally evaluate detection performance with a new MeerKAT-inspired
simulation dataset that provides a technical challenge for machine-learnt RFI
detection methods. This dataset focuses on satellite-based RFI, an increasingly
important class of RFI and is an additional contribution. Our approach remains
competitive with existing methods in AUROC, AUPRC and F1 scores for the HERA
dataset but exhibits difficulty in the LOFAR and Tabascal datasets. Our method
maintains this accuracy while completely removing the compute and
memory-intense latent sampling step found in NLN. This work demonstrates the
viability of SNNs as a promising avenue for machine-learning-based RFI
detection in radio telescopes by establishing a minimal performance baseline on
traditional and nascent satellite-based RFI sources and is the first work to
our knowledge to apply SNNs in astronomy.
