做SNN训练或神经形态计算的团队，LIF的脉冲不连续问题可能让你头疼——QIF神经元直接解决了这个痛点，性能更好且训练更稳定，值得在项目中试试替换。
脉冲神经网络（SNN）训练中广泛使用的LIF神经元存在参数微小变化导致脉冲消失/出现、损失景观碎片化的问题。最新研究发现，二次整合-发放（QIF）神经元能避免这些不连续性，实现连续平滑的梯度下降。在Spiking Heidelberg Digits数据集上，QIF网络通过超参数搜索后性能显著优于LIF网络。可视化显示LIF的损失景观更碎片化、梯度更不稳定，而QIF则更平滑。研究建议用QIF等连续脉冲动力学模型替代LIF进行梯度下降训练。
Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing. However, for the extensively used leaky integrate-and-fire (LIF) neurons, arbitrarily small parameter changes can induce spike (dis)appearances that disrupt subsequent activity, leading to unstable neural representations and permanently silent neurons during exact spike-based gradient descent. Recent work shows that a class of neuron models, which includes the quadratic integrate-and-fire (QIF) neuron, avoids these discontinuities and enables continuous and even smooth spike-based gradient descent. However, it remains unclear whether these advantages translate into practice. Here, we demonstrate that they do so via a controlled comparison between networks of LIF and QIF neurons on the popular Spiking Heidelberg Digits dataset. Specifically, in a first step, we perform a thorough hyperparameter search to optimize both models, revealing a clear performance advantage of QIF neurons. In a second step, we visualize the loss and gradient landscapes. Consistent with their inferior performance, we find that the loss landscapes of LIF neurons, which are discontinuous, appear more fragmented and the related gradients more erratic. An analysis of the landscapes of single samples indicates that these features arise from changes in the temporal order of spikes, which often cause disruptive spike (dis)appearances. Overall, our results advocate replacing LIF neurons with neuron models exhibiting continuous spiking dynamics, such as QIF neurons, for gradient descent training.