Abstract
Spiking neural networks (SNNs) are artificial neural networks based on simulated biological neurons and have attracted much attention in recent artificial intelligence technology studies. The dendrites in biological neurons have efficient information processing ability and computational power; however, the neurons of SNNs rarely match the complex structure of the dendrites. Inspired by the nonlinear structure and highly sparse properties of neuronal dendrites, in this study, we propose an efficient, lightweight SNN method with nonlinear pruning and dendritic integration (NSPDI-SNN). In this method, we introduce nonlinear dendritic integration (NDI) to improve the representation of the spatiotemporal information of neurons. We implement heterogeneous state transition ratios of dendritic spines and construct a new and flexible nonlinear synaptic pruning (NSP) method to achieve the high sparsity of SNN. We conducted systematic experiments on three benchmark datasets (DVS128 Gesture, CIFAR10-DVS, and CIFAR10) and extended the evaluation to two complex tasks (speech recognition and reinforcement learning-based maze navigation task). Across all tasks, NSPDI-SNN consistently achieved high sparsity with minimal performance degradation. In particular, our method achieved the best experimental results on all three event stream datasets. Further analysis showed that NSPDI significantly improved the efficiency of synaptic information transfer as sparsity increased. In conclusion, our results indicate that the complex structure and nonlinear computation of neuronal dendrites provide a promising approach for developing efficient SNN methods.
Abstract (translated)
脉冲神经网络（SNNs）是基于模拟生物神经元的人工神经网络，在最近的人工智能技术研究中引起了广泛关注。生物神经元中的树突具有高效的信息处理能力和计算能力；然而，目前的SNN神经元很少能匹配到这种复杂的树突结构。受树突非线性结构和高度稀疏特性的启发，我们在本研究中提出了一种高效的、轻量级的方法——带非线性剪枝和树突发射整合（NSPDI-SNN）的脉冲神经网络方法。在此方法中，我们引入了非线性树突发射整合（NDI），以增强脉冲神经元时空信息的表示能力；同时，通过构建异质状态转换比例的树突棘结构，并建立了一种新的、灵活的非线性突触剪枝（NSP）方法，实现了SNN的高度稀疏化。 我们在三个基准数据集（DVS128手势识别、CIFAR10-DVS和CIFAR10）上进行了系统实验，并扩展到了两个复杂任务（语音识别和基于强化学习的迷宫导航任务）。在所有这些任务中，NSPDI-SNN始终能够在保持性能轻微下降的前提下达到高稀疏度。特别是在三个事件流数据集上，我们的方法取得了最佳的实验结果。 进一步分析表明，随着稀疏性的提高，NSP显著提升了突触信息传输效率。综上所述，我们的研究结果显示：神经元树突复杂的结构和非线性计算为开发高效的SNN方法提供了一种有前景的方法。
URL
https://arxiv.org/abs/2508.21566
