<p>Advanced large-scale neural interfaces call for efficient algorithms to automatically process and optimally exploit the richness of their heavy continuous flow of data. In this context, we introduce here a very frugal generic single-layer Spiking Neural Network (SNN) for fully unsupervised identification and classification of multivariate temporal patterns in continuous data streams. This approach is first validated on simulated multivariate data, Mel Cepstral representations of speech sounds, and multichannel multiunit neural recordings. Then, this very simple SNN was found to be effective at classifying action potentials in a fully unsupervised and online-compatible mode on simulated and real spike sorting datasets. These results pave the way for highly frugal SNN architectures for automatic unsupervised real-time pattern recognition in high-dimensional neural data recordings, which could be suitable for future embedding into ultra-low power hardware platforms, such as active neural implants.</p>