Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
This work addresses the need for more efficient and structured learning in neural networks, particularly for vision tasks, by incorporating neuro-inspired synchronization, though it appears incremental as it builds on existing Vision Transformer frameworks.
The authors tackled the problem of deep learning architectures neglecting oscillatory synchronization by introducing Kuramoto oscillatory Phase Encoding (KoPE) to Vision Transformers, resulting in improved training, parameter, and data efficiency for vision models and enhanced performance in tasks like semantic segmentation and few-shot visual reasoning.
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.