Spiking Neural Networks (SNNs) have received considerable attention not only
for their superiority in energy efficient 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 DNN, 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
transferbility and scalability. 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