Spike Driven

Spike-driven neural networks (SNNs) are emerging as energy-efficient alternatives to traditional artificial neural networks by mimicking the brain's event-driven processing. Current research focuses on improving SNN performance through novel architectures like Spiking Wavelet Transformers and point-based networks, and advanced coding schemes such as gated attention mechanisms, to address limitations in handling high-frequency information and efficiently processing sparse data from event cameras. These advancements aim to bridge the performance gap with traditional ANNs, leading to significant potential for low-power applications in areas like computer vision and robotics.