标题： 一种用于训练脉冲神经网络的精确梯度框架 显示英文标题

标题： An Exact Gradient Framework for Training Spiking Neural Networks

摘要： 脉冲神经网络本质上依赖于离散脉冲事件的精确时间来进行信息处理。 引入额外的生物启发自由度，如可训练的突触传输延迟和自适应的发放阈值，对于充分利用SNN的时序动态特性是至关重要的。 尽管最近的方法已经展示了在准确性和时间表示方面训练突触权重和延迟的好处，但这些技术通常依赖于离散时间仿真、替代梯度近似或对膜电位等内部状态变量的完全访问。 这些要求限制了训练的精度和效率，并由于增加了内存和I/O带宽需求，给神经形态硬件实现带来了挑战。 为了克服这些挑战，我们提出了一种分析事件驱动的学习框架，该框架不仅相对于突触权重和传输延迟，还相对于自适应的神经元发放阈值计算精确的损失梯度。 在多个基准测试中的实验表明，与现有方法相比，准确性（最高达7%）、时间精度和鲁棒性都有显著提升。 显示英文摘要

摘要： Spiking neural networks inherently rely on the precise timing of discrete spike events for information processing. Incorporating additional bio-inspired degrees of freedom, such as trainable synaptic transmission delays and adaptive firing thresholds, is essential for fully leveraging the temporal dynamics of SNNs. Although recent methods have demonstrated the benefits of training synaptic weights and delays, both in terms of accuracy and temporal representation, these techniques typically rely on discrete-time simulations, surrogate gradient approximations, or full access to internal state variables such as membrane potentials. Such requirements limit training precision and efficiency and pose challenges for neuromorphic hardware implementation due to increased memory and I/O bandwidth demands. To overcome these challenges, we propose an analytical event-driven learning framework that computes exact loss gradients not only with respect to synaptic weights and transmission delays but also to adaptive neuronal firing thresholds. Experiments on multiple benchmarks demonstrate significant gains in accuracy (up to 7%), timing precision, and robustness compared to existing methods.