Spiking neural networks (SNNs) have attracted much attention due to their ability to process temporal information, low power consumption, and higher biological plausibility. However, it is still challenging to develop efficient and high-performing learning algorithms for SNNs. Methods like artificial neural network (ANN)-to-SNN conversion can transform ANNs to SNNs with slight performance loss, but it needs a long simulation to approximate the rate coding. Directly training SNN by spike-based backpropagation (BP) such as surrogate gradient approximation is more flexible. Yet now, the performance of SNNs is not competitive compared with ANNs. In this paper, we propose a novel k-based leaky Integrate-and-Fire (KLIF) neuron model to improve the learning ability of SNNs. Compared with the popular leaky integrate-and-fire (LIF) model, KLIF adds a learnable scaling factor to dynamically update the slope and width of the surrogate gradient curve during training and incorporates a ReLU activation function that selectively delivers membrane potential to spike firing and resetting. The proposed spiking unit is evaluated on both static MNIST, Fashion-MNIST, CIFAR-10 datasets, as well as neuromorphic N-MNIST, CIFAR10-DVS, and DVS128-Gesture datasets. Experiments indicate that KLIF performs much better than LIF without introducing additional computational cost and achieves state-of-the-art performance on these datasets with few time steps. Also, KLIF is believed to be more biological plausible than LIF. The good performance of KLIF can make it completely replace the role of LIF in SNN for various tasks.

翻译：脉冲神经网络（SNN）因其处理时间信息的能力、低功耗和更高的生物合理性而受到广泛关注。然而，为SNN开发高效且高性能的学习算法仍具有挑战性。人工神经网络（ANN）到SNN的转换等方法可以在性能略有损失的情况下将ANN转换为SNN，但需要较长的模拟来近似速率编码。通过基于脉冲的反向传播（BP）（如替代梯度近似）直接训练SNN更为灵活，但目前SNN的性能与ANN相比仍不具竞争力。本文提出了一种新型的基于k的漏积分激发（KLIF）神经元模型，以提升SNN的学习能力。与流行的漏积分激发（LIF）模型相比，KLIF增加了一个可学习的缩放因子，用于在训练过程中动态更新替代梯度曲线的斜率和宽度，并整合了ReLU激活函数，该函数选择性地将膜电位传递至脉冲触发和重置过程。所提出的脉冲单元在静态MNIST、Fashion-MNIST、CIFAR-10数据集以及神经形态N-MNIST、CIFAR10-DVS和DVS128-Gesture数据集上进行了评估。实验表明，KLIF在不引入额外计算成本的情况下性能显著优于LIF，并在这些数据集上以较少的时间步达到了最先进的性能。此外，KLIF被认为比LIF更具生物合理性。KLIF的良好性能使其能够完全取代LIF在SNN中的角色，适用于各种任务。