Lateral connection is a fundamental feature of biological neural circuits, facilitating local information processing and adaptive learning. In this work, we integrate lateral connections with a substructure selection network to develop a novel diffusion model based on spiking neural networks (SNNs). Unlike conventional artificial neural networks, SNNs employ an intrinsic spiking inner loop to process sequential binary spikes. We leverage this spiking inner loop alongside a lateral connection mechanism to iteratively refine the substructure selection network, enhancing model adaptability and expressivity. Specifically, we design a lateral connection framework comprising a learnable lateral matrix and a lateral mapping function, both implemented using spiking neurons, to dynamically update lateral connections. Through mathematical modeling, we establish that the proposed lateral update mechanism, under a well-defined local objective, aligns with biologically plausible synaptic plasticity principles. Extensive experiments validate the effectiveness of our approach, analyzing the role of substructure selection and lateral connection during training. Furthermore, quantitative comparisons demonstrate that our model consistently surpasses state-of-the-art SNN-based generative models across multiple benchmark datasets.

翻译：侧向连接是生物神经回路的基本特征，能够促进局部信息处理与自适应学习。本研究将侧向连接与子结构选择网络相结合，提出了一种基于脉冲神经网络的新型扩散模型。与传统人工神经网络不同，脉冲神经网络采用固有的脉冲内循环来处理序列化的二进制脉冲。我们利用这种脉冲内循环机制与侧向连接结构，迭代优化子结构选择网络，从而提升模型的适应性与表达能力。具体而言，我们设计了一个包含可学习侧向矩阵与侧向映射函数的侧向连接框架，两者均通过脉冲神经元实现，以动态更新侧向连接。通过数学建模，我们证明了在明确定义的局部目标下，所提出的侧向更新机制符合生物学合理的突触可塑性原理。大量实验验证了本方法的有效性，并分析了训练过程中子结构选择与侧向连接的作用。此外，定量比较表明，我们的模型在多个基准数据集上持续超越基于脉冲神经网络的最先进生成模型。