SNN Model

Spiking neural networks (SNNs), inspired by the biological brain's event-driven processing, aim to create energy-efficient and biologically plausible artificial intelligence models. Current research focuses on improving training methods (e.g., surrogate gradient methods, direct feedback alignment, knowledge distillation), developing efficient architectures (including adaptations of convolutional and transformer networks), and optimizing SNN performance for specific applications like image classification, optical flow estimation, and keyword spotting. These advancements hold significant promise for low-power neuromorphic computing and could lead to more efficient and robust AI systems in various domains.