Recent advances in spiking neural networks (SNNs) have a predominant focus on network architectures, while relatively little attention has been paid to the underlying neuron model. The point neuron models, a cornerstone of deep SNNs, pose a bottleneck on the network-level expressivity since they depict somatic dynamics only. In contrast, the multi-compartment models in neuroscience offer remarkable expressivity by introducing dendritic morphology and dynamics, but remain underexplored in deep learning due to their unaffordable computational cost and inflexibility. To combine the advantages of both sides for a flexible, efficient yet more powerful model, we propose the dendritic spiking neuron (DendSN) incorporating multiple dendritic branches with nonlinear dynamics. Compared to the point spiking neurons, DendSN exhibits significantly higher expressivity. DendSN's flexibility enables its seamless integration into diverse deep SNN architectures. To accelerate dendritic SNNs (DendSNNs), we parallelize dendritic state updates across time steps, and develop Triton kernels for GPU-level acceleration. As a result, we can construct large-scale DendSNNs with depth comparable to their point SNN counterparts. Next, we comprehensively evaluate DendSNNs' performance on various demanding tasks. By modulating dendritic branch strengths using a context signal, catastrophic forgetting of DendSNNs is substantially mitigated. Moreover, DendSNNs demonstrate enhanced robustness against noise and adversarial attacks compared to point SNNs, and excel in few-shot learning settings. Our work firstly demonstrates the possibility of training bio-plausible dendritic SNNs with depths and scales comparable to traditional point SNNs, and reveals superior expressivity and robustness of reduced dendritic neuron models in deep learning, thereby offering a fresh perspective on advancing neural network design.

翻译：脉冲神经网络（SNNs）的最新进展主要集中于网络架构的改进，而对底层神经元模型的关注相对较少。作为深度SNNs基石的点神经元模型仅描述胞体动态，这限制了网络层面的表达能力。相比之下，神经科学中的多室模型通过引入树突形态和动态特性展现出卓越的表达能力，但由于其高昂的计算成本和缺乏灵活性，在深度学习领域仍未得到充分探索。为融合双方优势以构建灵活高效且更强大的模型，我们提出了包含多分支非线性动态的树突脉冲神经元（DendSN）。相较于点脉冲神经元，DendSN展现出显著更高的表达能力。其灵活性使其能够无缝集成到多种深度SNN架构中。为加速树突脉冲神经网络（DendSNNs）的计算，我们实现了跨时间步的树突状态并行更新，并开发了面向GPU加速的Triton内核。由此，我们能够构建与点SNN规模相当的深度大规模DendSNNs。随后，我们在多项高要求任务中对DendSNNs性能进行全面评估。通过上下文信号调控树突分支强度，DendSNNs的灾难性遗忘现象得到显著缓解。此外，与点SNNs相比，DendSNNs表现出更强的抗噪声和对抗攻击鲁棒性，并在小样本学习场景中表现优异。本研究首次证明了训练具有与传统点SNN相当深度和规模的生物合理性树突SNNs的可能性，揭示了简化树突神经元模型在深度学习中卓越的表达能力和鲁棒性，从而为推进神经网络设计提供了全新视角。