The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking Neural Network (SNN) based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the potential for integrating multiple biological principles. Existing researches on LSM focus on different certain perspectives, including high-dimensional encoding or optimization of the liquid layer, network architecture search, and application to hardware devices. There is still a lack of in-depth inspiration from the learning and structural evolution mechanism of the brain. Considering these limitations, this paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules. For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property. For brain-inspired learning of LSM, we propose a dopamine-modulated Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term dopamine regulation and local trace-based BCM synaptic plasticity. Comparative experimental results on different decision-making tasks show that introducing structural evolution of the liquid layer, and the DA-BCM regulation of the liquid layer and the readout layer could improve the decision-making ability of LSM and flexibly adapt to rule reversal. This work is committed to exploring how evolution can help to design more appropriate network architectures and how multi-scale neuroplasticity principles coordinated to enable the optimization and learning of LSMs for relatively complex decision-making tasks.

翻译：人脑经过数亿年进化形成的架构设计与多尺度学习原理，对于实现类人智能至关重要。基于脉冲神经网络（SNN）的液态机（LSM）因其仿脑结构及整合多种生物机制的潜力，成为研究类脑智能的合适架构。现有LSM研究聚焦于不同特定视角，包括高维编码或液态层优化、网络架构搜索及硬件设备应用，但对大脑学习与结构演化机制的深度借鉴仍显不足。针对这些局限，本文提出一种融合自适应结构演化与多尺度生物学习规则的新型LSM学习模型。在结构演化方面，开发了自适应可演化LSM模型，以优化具有分离特性的液态层神经架构设计；在LSM的类脑学习方面，提出多巴胺调控的Bienenstock-Cooper-Munros（DA-BCM）方法，整合全局长期多巴胺调节与局部痕量BCM突触可塑性。不同决策任务的对比实验表明，引入液态层结构演化及其与读出层的DA-BCM调控，可提升LSM的决策能力并灵活适应规则反转。本研究致力于探索演化如何帮助设计更优的网络架构，以及多尺度神经可塑性机制如何协同实现LSM对相对复杂决策任务的优化与学习。