Editorial

Gaetano Di Caterina, Malu Zhang, Jundong Liu

Abstract: Spiking Neural Networks (SNNs) process information through discrete, time-dependent spikes, closely mimicking the dynamics of biological neurons. This temporal coding enables SNNs to capture rich spatio-temporal patterns and exploit event-driven sensing and computation, offering sophisticated information 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 with significantly reduced power consumption, compared to conventional neural networks and deep learning models. Advances in learning algorithms, and theoretical and software frameworks have accelerated the development of SNNs, bringing neuroscience-inspired models to practical machine intelligence in engineering systems, while also reducing the performance gap from deep neural networks (DNNs). This special issue brings together cutting-edge research on a diverse set of practical applications of SNNs, such as EEG-based brain-computer interface (BCI), data encryption, and speech enhancement, building on the theoretical foundations of SNNs and demonstrating their transformative potential in addressing complex challenges.

External IDs:doi:10.3389/fnins.2026.1771268