Editorial: Theoretical advances and practical applications of spiking neural networks, volume II

Spiking Neural Networks (SNNs) process information through discrete, time-dependent spikes, closely 2 mimicking the dynamics of biological neurons. This temporal coding enables SNNs to capture rich spatio-3 temporal patterns and exploit event-driven sensing and computation, offering sophisticated information 4 processing capabilities and practical efficiency at the same time.The key strengths of SNNs reside in their biological plausibility and energy efficiency. SNNs can operate 6 with significantly reduced power consumption, compared to conventional neural networks and deep learning 7 models. Advances in learning algorithms, and theoretical and software frameworks have accelerated 8 the development of SNNs, bringing neuroscience-inspired models to practical machine intelligence in 9 engineering systems, while also reducing the performance gap from deep neural networks (DNNs).