Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional
artificial neural networks (ANNs) due to their unique spike-based event-driven nature …

Spiking neural networks (SNNs) are attracting widespread interest due to their biological
plausibility, energy efficiency, and powerful spatiotemporal information representation …

Spiking neural networks (SNNs) have recently demonstrated outstanding performance in a
variety of high-level tasks, such as image classification. However, advancements in the field …

This review aims to contribute to the quest for artificial general intelligence by examining
neuroscience and cognitive psychology methods for potential inspiration. Despite the …

Neurons are crucial components of neural networks, but implementing biologically accurate
neuron models in hardware is challenging due to their nonlinearity and time variance. This …