Spikes as Detectors: Phase-Conditioned Spiking Dynamics for Time-Series Anomaly Detection

Most time-series anomaly detectors ask whether an observation can be predicted or reconstructed. We ask a different question: can a detector’s own spiking dynamics become anomaly evidence? We study this question for periodic and quasi-periodic time series and propose SPIRE, a phase-conditioned spiking anomaly detector. SPIRE uses period-aware current formation to expose phase-aligned cross-period residuals to leaky integrate-and-fire neurons. We refer to this conversion as eventification: after residual injection, recurrence violations induce abnormal bursts, silences, or phase-shifted spike trains. SPIRE then constructs phase-conditioned normal templates of spike states and scores deviations in residual energy, firing-rate patterns, and burst/silence statistics, instead of relying solely on output-space prediction or reconstruction errors. This separates the operational protocol, including causal current-step detection, one-step prediction, and non-causal reconstruction, from the source of anomaly evidence, namely output errors, spike dynamics, or both. On TSB-AD-U and TSB-AD-M, SPIRE establishes new best VUS-PR among neural time-series anomaly detectors. Spike-native scores remain competitive without reconstruction error, hybrid scoring consistently improves error-only scoring, and replacing LIF neurons with continuous activations in matched ANN counterparts reduces detection performance under the same period-aware architecture. Periodicity-stratified analyses and spike-state visualizations further show that the gains are strongest when phase recurrence is reliable, supporting the proposed spike-as-detector mechanism. These results suggest that phase-conditioned spiking dynamics can serve as a first-class anomaly detection signal for periodic time series, rather than merely an efficient computational substrate.