March 3, 2026Open Access

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

Puntos clave

Spiking neural networks process information with energy efficiency, mimicking biological neurons' dynamics.
Key strengths include reduced power consumption compared to deep learning models, enhancing practical machine intelligence.
Advancements in learning algorithms and frameworks have driven spiking neural networks closer to deep neural networks' performance.
The spatio-temporal pattern capture of SNNs supports sophisticated information processing across various applications.

Resumen

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).

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