# Learning Spiking Neural Network from Easy to Hard task

arXiv: https://arxiv.org/abs/2309.04737 · HTML: https://mlsanity.com/paper/2309.04737/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 47 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 27 of 27)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration](https://mlsanity.com/paper/2106.06984/) (2021)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022)
- [Supervised learning based on temporal coding in spiking neural networks](https://mlsanity.com/paper/1606.08165/) (2016)
- [Gradient Descent for Spiking Neural Networks](https://mlsanity.com/paper/1706.04698/) (2017)
- [Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception](https://mlsanity.com/paper/1807.10936/) (2018)
- [Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks](https://mlsanity.com/paper/2007.01204/) (2020)
- [Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1908.00262/) (2019)
- [Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient](https://mlsanity.com/paper/2001.01587/) (2020)
- [Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection](https://mlsanity.com/paper/1804.01552/) (2018)
- [An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications](https://mlsanity.com/paper/1910.01059/) (2019)
- [BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons](https://mlsanity.com/paper/2105.13004/) (2021)
- [Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition](https://mlsanity.com/paper/2302.01194/) (2023)
- [Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation](https://mlsanity.com/paper/2010.04434/) (2020)
- [Unsupervised Learning with Self-Organizing Spiking Neural Networks](https://mlsanity.com/paper/1807.09374/) (2018)
- [Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM](https://mlsanity.com/paper/1812.01967/) (2018)
- [Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution](https://mlsanity.com/paper/2003.06321/) (2020)
- [Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration](https://mlsanity.com/paper/2202.06821/) (2022)
- [Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints](https://mlsanity.com/paper/1701.03647/) (2017)

## Abstract (arXiv)

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.