Spiking neural networks is a computational paradigm within artificial intelligence and computational neuroscience that models neural activity using discrete temporal events called "spikes." This approach fundamentally differs from rate-based artificial neural networks by encoding information in the timing and patterns of these events, investigating biologically plausible mechanisms for efficient processing of temporal data and exploring brain-inspired computational principles.