Spiking neural networks (SNNs) have demonstrated significant potential in real-time multi-sensor perception tasks due to their event-driven and parameter-efficient characteristics. A key challenge is the timestep-wise iterative update of neuronal hidden states (membrane potentials), which complicates the trade-off between accuracy and latency. SNNs tend to achieve better performance with longer timesteps, inevitably resulting in higher computational overhead and latency compared to artificial neural networks (ANNs). Moreover, many recent advances in SNNs rely on architecture-specific optimizations, which, while effective with fewer timesteps, often limit generalizability and scalability across modalities and models. To address these limitations, we propose Activation-wise Membrane Potential Propagation (AMP2), a unified hidden state update mechanism for SNNs. Inspired by the spatial propagation of membrane potentials in biological neurons, AMP2 enables dynamic transmission of membrane potentials among spatially adjacent neurons, facilitating spatiotemporal integration and cooperative dynamics of hidden states, thereby improving efficiency and accuracy while reducing reliance on extended temporal updates. This simple yet effective strategy significantly enhances SNN performance across various architectures, including MLPs and CNNs for point cloud and event-based data. Furthermore, ablation studies integrating AMP2 into Transformer-based SNNs for classification tasks demonstrate its potential as a general-purpose and efficient solution for spiking neural networks.

Spiking reinforcement learning (SRL) has gradually received attention because of its ultra-low energy consumption, but the current SRL algorithms are almost online algorithms that are sample inefficient. It is well known that pure offline spiking reinforcement (offline RL) learning has limited performance. Therefore, we study SRL in the offline-to-online setting, which simultaneously possesses the advantages of low energy consumption and sample efficiency. To the best of our knowledge, this is the first study for offline-to-online SRL. Like offline-to-online RL, offline-to-online SRL also has the policy collapse issue. To overcome this problem, we adaptively adjust the penalty of behavior cloning term of spiking TD3+BC (SpikTD3+BC) based on the adaptability of policy to environment and propose a stable offline-to-online SRL, named AdaSpikTD3+BC. Experimental results on the D4RL benchmark tasks show that AdaSpikTD3+BC can not only avoid policy collapse but also cost about 10% energy to approach the performance of offline-to-online RL based on DNN.