Spiking Neural Networks (SNNs) are more biologically plausible and computationally efficient. Therefore, SNNs have the natural advantage of drawing the sparse structural plasticity of brain development to alleviate the energy problems of deep neural networks caused by their complex and fixed structures. However, previous SNNs compression works are lack of in-depth inspiration from the brain development plasticity mechanism. This paper proposed a novel method for the adaptive structural development of SNN (SD-SNN), introducing dendritic spine plasticity-based synaptic constraint, neuronal pruning and synaptic regeneration. We found that synaptic constraint and neuronal pruning can detect and remove a large amount of redundancy in SNNs, coupled with synaptic regeneration can effectively prevent and repair over-pruning. Moreover, inspired by the neurotrophic hypothesis, neuronal pruning rate and synaptic regeneration rate were adaptively adjusted during the learning-while-pruning process, which eventually led to the structural stability of SNNs. Experimental results on spatial (MNIST, CIFAR-10) and temporal neuromorphic (N-MNIST, DVS-Gesture) datasets demonstrate that our method can flexibly learn appropriate compression rate for various tasks and effectively achieve superior performance while massively reducing the network energy consumption. Specifically, for the spatial MNIST dataset, our SD-SNN achieves 99.51\% accuracy at the pruning rate 49.83\%, which has a 0.05\% accuracy improvement compared to the baseline without compression. For the neuromorphic DVS-Gesture dataset, 98.20\% accuracy with 1.09\% improvement is achieved by our method when the compression rate reaches 55.50\%.

翻译：脉冲神经网络（SNN）具有更强的生物合理性和计算高效性，因此其天然具备借鉴大脑发育中稀疏结构可塑性的优势，可缓解深度神经网络因结构复杂固定导致的能耗问题。然而，现有SNN压缩工作缺乏对大脑发育可塑性机制的深度启发。本文提出一种新型SNN自适应结构发育方法（SD-SNN），引入基于树突棘可塑性的突触约束、神经元剪枝与突触再生机制。研究发现，突触约束与神经元剪枝可检测并消除SNN中的大量冗余，结合突触再生能有效防止并修复过度剪枝。此外，受神经营养假说启发，在边学习边剪枝过程中自适应调节神经元剪枝率与突触再生率，最终实现SNN结构稳定性。在空间数据集（MNIST、CIFAR-10）与时间神经形态数据集（N-MNIST、DVS-Gesture）上的实验结果表明，本方法可灵活学习不同任务所需的合适压缩率，在显著降低网络能耗的同时有效实现卓越性能。具体而言，针对空间MNIST数据集，SD-SNN在49.83%剪枝率下达到99.51%准确率，相比未压缩基线提升0.05%准确率；针对神经形态DVS-Gesture数据集，在压缩率达55.50%时，本方法实现98.20%准确率，提升1.09%。