为静态图像设计可学习的时间编码，提升脉冲神经网络表现
Revisiting Direct Encoding: Learnable Temporal Dynamics for Static Image Spiking Neural Networks
- 引入自适应相位偏移的轻量级时间编码机制
- 在多个数据集上实现媲美传统编码的准确率
- 适合研究脉冲神经网络时序建模的学者参考
处理缺乏内在时序动态的静态图像仍是脉冲神经网络（SNNs）的核心挑战。直接训练的SNN通常将静态输入重复多时间步，导致时序维度退化为类似率编码的表示，难以实现有意义的时序建模。本文重新审视直接编码与速率编码之间的性能差距，发现其主要源于卷积可学习性及替代梯度形式，而非编码方式本身。为阐明这一机制，我们提出一种最小化可学习时间编码，通过引入自适应相位偏移，从静态输入中激发有意义的时序变化。
原文摘要 · Abstract (English)
Handling static images that lack inherent temporal dynamics remains a fundamental challenge for spiking neural networks (SNNs). In directly trained SNNs, static inputs are typically repeated across time steps, causing the temporal dimension to collapse into a rate like representation and preventing meaningful temporal modeling. This work revisits the reported performance gap between direct and rate based encodings and shows that it primarily stems from convolutional learnability and surrogate gradient formulations rather than the encoding schemes themselves. To illustrate this mechanism level clarification, we introduce a minimal learnable temporal encoding that adds adaptive phase shifts to induce meaningful temporal variation from static inputs.
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