Beyond Timesteps: A Novel Activation-wise Membrane Potential Propagation Mechanism for Spiking Neural Networks in 3D cloud

Due to the similar characteristics between event-based visual data and point clouds, recent studies have emerged that treat event data as event clouds to learn based on point cloud analysis. Additionally, some works approach point clouds from the perspective of event vision, employing Spiking Neural Network (SNN) due to their asynchronous nature. However, these contributions are often domain-specific, making it difficult to extend their applicability to other intersecting fields. Moreover, while SNN-based visual tasks have seen significant growth, the conventional timestep-wise iterative activation strategy largely limits their real-world applications by large timesteps, resulting in significant delays and increased computational costs. Although some innovative methods achieve good performance with short timesteps (<10), few have fundamentally restructured the update strategy of spiking neurons to completely overcome the limitations of timesteps. In response to these concerns, we propose a novel and general activation strategy for spiking neurons called Activation-wise Membrane Potential Propagation (AMP2). This approach extends the concept of timesteps from a manually crafted parameter within the activation function to any existing network structure. In experiments on common point cloud tasks (classification, object, and scene segmentation) and event cloud tasks (action recognition), we found that AMP2 stabilizes SNN training, maintains competitive performance, and reduces latency compared to the traditional timestep-wise activation paradigm.