Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in deep recurrent architectures with sparse connectivity remains a major challenge. In this work, we propose a structured multi-layer recurrent SNN architecture composed of locally dense recurrent layers augmented with sparse small-world long-range projections to a readout population. The long-range connectivity is largely fixed, preserving routing efficiency and hardware scalability, while synaptic adaptation is performed using strictly local plasticity mechanisms. To enable supervised learning without backpropagation or surrogate gradients, we introduce a biologically motivated learning framework that combines: (i) population-based winner-take-all (WTA) teaching signals at the output layer, (ii) fixed random broadcast alignment feedback pathways, and (iii) low-dimensional modulatory neuron populations that gate synaptic updates through three-factor learning rules with eligibility traces. This design supports deep recurrent computation with sparse global communication and purely local synaptic updates. We analyze the algorithmic properties, computational complexity, and hardware feasibility of the proposed approach, and demonstrate stable learning and competitive performance on benchmark classification tasks. The results highlight the potential of structured recurrence and neuromodulatory learning to enable scalable, hardware-compatible SNN training beyond gradient-based methods.

翻译：脉冲神经网络（SNN）为低能耗和生物合理性计算提供了有前景的框架；然而，在具有稀疏连接的深度循环架构中实现可扩展学习仍是一大挑战。本文提出一种结构化多层循环脉冲神经网络架构，该架构由局部密集的循环层组成，并辅以稀疏的小世界长程投射连接至读出群体。长程连接基本保持固定，以维持路由效率和硬件可扩展性，而突触适应则通过严格局部的可塑性机制完成。为实现无反向传播或替代梯度的监督学习，我们引入一种生物合理性学习框架，该框架结合了：(i) 基于群体的胜者全取（WTA）教学信号作用于输出层，(ii) 固定的随机广播对齐反馈通路，以及(iii) 通过具有资格迹的三因子学习规则调控突触更新的低维调制神经元群体。该设计支持具有稀疏全局通信和纯局部突触更新的深度循环计算。我们分析了所提方法的算法特性、计算复杂度及硬件可行性，并在基准分类任务上展示了稳定的学习效果与具有竞争力的性能。结果凸显了结构化循环与神经调制学习在超越基于梯度方法、实现可扩展且硬件兼容的SNN训练方面的潜力。