  The complex and unique neural network topology of the human brain formed
through natural evolution enables it to perform multiple cognitive functions
simultaneously. Automated evolutionary mechanisms of biological network
structure inspire us to explore efficient architectural optimization for
Spiking Neural Networks (SNNs). Instead of manually designed fixed
architectures or hierarchical Network Architecture Search (NAS), this paper
evolves SNNs architecture by incorporating brain-inspired local modular
structure and global cross-module connectivity. Locally, the brain
region-inspired module consists of multiple neural motifs with excitatory and
inhibitory connections; Globally, we evolve free connections among modules,
including long-term cross-module feedforward and feedback connections. We
further introduce an efficient multi-objective evolutionary algorithm based on
a few-shot performance predictor, endowing SNNs with high performance,
efficiency and low energy consumption. Extensive experiments on static datasets
(CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS, DVS128-Gesture)
demonstrate that our proposed model boosts energy efficiency, archiving
consistent and remarkable performance. This work explores brain-inspired neural
architectures suitable for SNNs and also provides preliminary insights into the
evolutionary mechanisms of biological neural networks in the human brain.
