Regarded as the third generation of neural networks, Spiking Neural Networks (SNNs) have garnered significant traction due to their biological plausibility and energy efficiency. Recent advancements in large models necessitate spiking neurons capable of high performance, adaptability, and training efficiency. In this work, we first propose a novel functional perspective that provides general guidance for designing the new generation of spiking neurons. Following the insightful guidelines, we propose the Adaptive Spiking Neuron (ASN), which incorporates trainable parameters to learn membrane potential dynamics and enable adaptive firing. ASN adopts an integer training and spike inference paradigm, facilitating efficient SNN training. To further enhance robustness, we propose a specialized variant of ASN, the Normalized Adaptive Spiking Neuron (NASN), which integrates normalization to stabilize training. We evaluate our neuron model on 19 datasets spanning five distinct tasks in both vision and language modalities, demonstrating the effectiveness and versatility of the ASN family. Our ASN family is expected to become the new generation of general-purpose spiking neurons.

翻译：被誉为第三代神经网络的脉冲神经网络（SNNs）因其生物 plausible 性和能效优势而受到广泛关注。大模型的最新进展要求脉冲神经元具备高性能、自适应性及训练效率。本文首先提出一种新颖的功能视角，为设计新一代脉冲神经元提供通用指导。基于该洞见性准则，我们提出自适应脉冲神经元（ASN），它引入可训练参数来学习膜电位动力学并实现自适应放电。ASN采用整数训练与脉冲推理范式，有助于高效训练SNN。为进一步增强鲁棒性，我们提出ASN的专用变体——归一化自适应脉冲神经元（NASN），通过集成归一化来稳定训练。我们在涵盖视觉和语言模态的19个数据集（跨越五项不同任务）上评估了所提出的神经元模型，验证了ASN系列的有效性与通用性。预期ASN系列将成为新一代通用脉冲神经元。