Emergence of brain-inspired small-world spiking neural network through neuroevolution
Wenxuan Pan, Feifei Zhao, Bing Han, Yiting Dong, Yi Zeng
Abstract
Studies suggest that the brain's high efficiency and low energy consumption may be closely related to its small-world topology and critical dynamics. However, existing efforts on the performance-oriented structural evolution of spiking neural networks (SNNs) are time-consuming and ignore the core structural properties of the brain. Here, we introduce a multi-objective Evolutionary Liquid State Machine (ELSM), which blends the small-world coefficient and criticality to evolve models and guide the emergence of brain-inspired, efficient structures. Experiments reveal ELSM's consistent and comparable performance, achieving 97.23% on NMNIST and outperforming LSM models on MNIST and Fashion-MNIST with 98.12% and 88.81% accuracies, respectively. Further analysis shows its versatility and spontaneous evolution of topologies such as hub nodes, short paths, long-tailed degree distributions, and numerous communities. This study evolves recurrent spiking neural networks into brain-inspired energy-efficient structures, showcasing versatility in multiple tasks and potential for adaptive general artificial intelligence.