Encoding static images into spike trains is a crucial step for enabling Spiking Neural Networks (SNNs) to process visual information efficiently. However, existing schemes such as rate coding, Poisson encoding, and time-to-first-spike (TTFS) often ignore spatial relationships and yield temporally inconsistent spike patterns. In this article, a novel cluster-based encoding approach is proposed, which leverages local density computation to preserve semantic structure in both spatial and temporal domains. This method introduces a 2D spatial cluster trigger that identifies foreground regions through connected component analysis and local density estimation. Then, extend to a 3D spatio-temporal (ST3D) framework that jointly considers temporal neighborhoods, producing spike trains with improved temporal consistency. Experiments on the N-MNIST dataset demonstrate that our ST3D encoder achieves 98.17% classification accuracy with a simple single-layer SNN, outperforming standard TTFS encoding (97.58%) and matching the performance of more complex deep architectures while using significantly fewer spikes (~3800 vs ~5000 per sample). The results demonstrate that this approach provides an interpretable and efficient encoding strategy for neuromorphic computing applications.

翻译：将静态图像编码为脉冲序列是实现脉冲神经网络高效处理视觉信息的关键步骤。然而，现有编码方案如频率编码、泊松编码和首次脉冲时间编码往往忽略空间关联性，且产生的脉冲模式在时间维度上缺乏一致性。本文提出一种基于集群的新型编码方法，该方法通过局部密度计算在空间和时间域中同时保持语义结构。该方法引入二维空间集群触发器，通过连通分量分析和局部密度估计识别前景区域。随后扩展至三维时空框架，该框架联合考虑时间邻域信息，生成具有更高时间一致性的脉冲序列。在N-MNIST数据集上的实验表明，我们的ST3D编码器结合单层脉冲神经网络可实现98.17%的分类准确率，优于标准首次脉冲时间编码方案（97.58%），并与更复杂的深层架构性能相当，同时显著减少单样本脉冲发放数量（约3800次对比约5000次）。实验结果证明，该方法为神经形态计算应用提供了一种可解释且高效的编码策略。