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.

引入课程学习（Curriculum Learning）的 CL-SNN 模型旨在提高脉冲神经网络（Spiking Neural Networks）的生物学可行性，通过使用置信度感知损失来区分和处理具有不同难度级别的样本，模型自动减少难样本对参数优化的贡献，实验证明其有效性。