Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL.

翻译：从简单的小概念开始，逐步引入复杂和困难的概念是人类学习的自然过程。脉冲神经网络（SNNs）旨在模拟人类处理信息的方式，但当前的SNN模型对所有样本一视同仁，这不符合人类学习原理，也忽视了SNN的生物可解释性。为解决此问题，我们提出一种CL-SNN模型，将课程学习（CL）引入SNN，使SNN更像人类一样学习，并提供更高的生物可解释性。CL是一种训练策略，主张先向模型呈现较简单的数据，再逐步引入更具挑战性的数据，以此模拟人类学习过程。我们采用置信度感知损失来度量并处理不同难度的样本。通过学习不同样本的置信度，模型自动降低困难样本对参数优化的贡献。我们在静态图像数据集MNIST、Fashion-MNIST、CIFAR10以及神经形态数据集N-MNIST、CIFAR10-DVS、DVS-Gesture上进行了实验，结果令人满意。据我们所知，这是首次通过引入CL来增强SNN生物可解释性的研究。