Single Spike

Single-spike spiking neural networks (SNNs) are a class of energy-efficient artificial neural networks that constrain neurons to fire at most once, offering advantages in low-power computing. Current research focuses on improving training algorithms for these networks, addressing challenges like slow convergence and limited computational capacity compared to multi-spike SNNs, while exploring the trade-offs between energy efficiency, accuracy, and robustness. This research is significant because it aims to unlock the full potential of SNNs for applications requiring low-power consumption and high speed, such as neuromorphic computing and edge devices.