Recently, SyncMap pioneered an approach to learn complex structures from sequences as well as adapt to any changes in underlying structures. This is achieved by using only nonlinear dynamical equations inspired by neuron group behaviors, i.e., without loss functions. Here we propose Symmetrical SyncMap that goes beyond the original work to show how to create dynamical equations and attractor-repeller points which are stable over the long run, even dealing with imbalanced continual general chunking problems (CGCPs). The main idea is to apply equal updates from negative and positive feedback loops by symmetrical activation. We then introduce the concept of memory window to allow for more positive updates. Our algorithm surpasses or ties other unsupervised state-of-the-art baselines in all 12 imbalanced CGCPs with various difficulties, including dynamically changing ones. To verify its performance in real-world scenarios, we conduct experiments on several well-studied structure learning problems. The proposed method surpasses substantially other methods in 3 out of 4 scenarios, suggesting that symmetrical activation plays a critical role in uncovering topological structures and even hierarchies encoded in temporal data.

翻译：最近，SyncMap开创了一种从序列中学习复杂结构并适应底层结构变化的方法。该方法仅通过受神经元群体行为启发的非线性动力学方程实现，无需使用损失函数。本文提出的对称同步映射超越了原始工作，展示了如何构建长期稳定的动力学方程与吸引-排斥点，即便面对不平衡的持续一般分块问题（CGCPs）也能有效应对。其核心思想是通过对称激活实现负反馈与正反馈回路的均衡更新。随后我们引入记忆窗口概念以增加正反馈更新。该算法在全部12个不同难度的不平衡CGCPs（包括动态变化场景）中，超越或持平其他无监督最先进基线方法。为验证其在真实场景中的性能，我们在多个经典结构学习问题上进行了实验。所提方法在4个场景中有3个显著优于其他方法，表明对称激活对于揭示时序数据中编码的拓扑结构乃至层级关系具有关键作用。