Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficiency with discrete signal processing but also for their natural suitability to integrate multi-scale biological plasticity. However, most SNNs directly adopt the structure of the well-established Deep Neural Networks (DNNs), and rarely automatically design Neural Architecture Search (NAS) for SNNs. The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture. In this paper, we propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously considering micro-, meso- and macro-scale brain topologies as the evolutionary search space. MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs). This training-free fitness function could greatly reduce computational consumption and NAS's time, and its task-independent property enables the searched SNNs to exhibit excellent transferability on multiple datasets. Furthermore, MSE-NAS show robustness against the training method and noise. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also illustrates the significant performance improvement and consistent bio-interpretability deriving from the topological evolution at different scales and the RDMs fitness function.

翻译：脉冲神经网络（SNNs）不仅因其基于离散信号处理的能效优势受到广泛关注，还因其天然适合整合多尺度生物可塑性而备受瞩目。然而，当前大多数SNN直接沿用成熟深度神经网络（DNNs）的结构，很少有研究为SNN自动设计神经架构搜索（NAS）。人脑的神经基序拓扑结构、模块化区域结构及跨脑区全局连接是自然进化的产物，可为设计类脑SNN架构提供完美参考。本文提出一种面向SNN的多尺度进化神经架构搜索方法（MSE-NAS），同时将微观、中观和宏观尺度的脑拓扑结构作为进化搜索空间。MSE-NAS通过类脑间接评估函数——表征差异性矩阵（RDMs），进化单个神经元操作、多电路基序的自组织集成以及基序间的全局连接。这种免训练适应度函数可大幅降低计算消耗与NAS耗时，其任务无关特性使得搜索得到的SNN在多个数据集上展现出优异的迁移能力。此外，MSE-NAS对训练方法和噪声均具有鲁棒性。大量实验表明，所提算法在静态数据集（CIFAR10、CIFAR100）和神经形态数据集（CIFAR10-DVS、DVS128-Gesture）上，以更短的模拟步长实现了最先进（SOTA）性能。深入分析还揭示了不同尺度的拓扑进化与RDMs适应度函数带来的显著性能提升及一致的生物可解释性。