Human brain is the product of evolution during hundreds over millions of years and can engage in multiple advanced cognitive functions with low energy consumption. Brain-inspired artificial intelligence serves as a computational continuation of this natural evolutionary process, is imperative to take inspiration from the evolutionary mechanisms of brain structure and function. Studies suggest that the human 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. In this paper, we propose a multi-objective Evolutionary Liquid State Machine (ELSM) with the combination of small-world coefficient and criticality as evolution goals and simultaneously integrate the topological properties of spiking neural networks from static and dynamic perspectives to guide the emergence of brain-inspired efficient structures.

翻译：人脑是经过数百万年进化的产物，能够以低能耗完成多种高级认知功能。作为这一自然进化过程的计算延续，受脑启发的人工智能必须借鉴大脑结构与功能的进化机制。研究表明，人脑的高效性与低能耗可能与其小世界拓扑结构和临界动力学特征密切相关。然而，现有面向性能驱动的脉冲神经网络结构进化研究不仅耗时，且忽视了大脑的核心结构特性。本文提出一种多目标进化液体状态机，将小世界系数与临界性共同作为进化目标，同时从静态与动态双重视角整合脉冲神经网络的拓扑特性，以引导受脑启发的类脑高效结构涌现。