Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordination such as feature binding. By contrast, most deep learning architectures represent and propagate information through activation values, neglecting the joint dynamics of rate and phase. In this work, we introduce Kuramoto oscillatory Phase Encoding (KoPE) as an additional, evolving phase state to Vision Transformers, incorporating a neuro-inspired synchronization mechanism to advance learning efficiency. We show that KoPE can improve training, parameter, and data efficiency of vision models through synchronization-enhanced structure learning. Moreover, KoPE benefits tasks requiring structured understanding, including semantic and panoptic segmentation, representation alignment with language, and few-shot abstract visual reasoning (ARC-AGI). Theoretical analysis and empirical verification further suggest that KoPE can accelerate attention concentration for learning efficiency. These results indicate that synchronization can serve as a scalable, neuro-inspired mechanism for advancing state-of-the-art neural network models.

翻译：时空神经动力学与振荡同步在生物信息处理中广泛存在，被假设支持特征绑定等灵活协调机制。然而，大多数深度学习架构通过激活值表示和传递信息，忽略了发放率与相位的联合动力学特性。本研究将Kuramoto振荡器相位编码（KoPE）作为增量演化相位状态引入视觉Transformer，通过神经启发的同步机制提升学习效率。我们证明，KoPE能通过同步增强结构学习提升视觉模型的训练效率、参数效率与数据效率。此外，KoPE在需要结构化理解的任务中表现优异，包括语义分割、全景分割、语言表征对齐及少样本抽象视觉推理（ARC-AGI）。理论分析与实验验证进一步表明，KoPE可通过加速注意力集中提升学习效率。这些结果揭示，同步机制可作为可扩展的神经启发方法推动前沿神经网络模型的发展。