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.

通过使用非线性动力学方程和对称激活，对于长期稳定性和解决不平衡连续通用分块问题（CGCPs）等困难，Symmetrical SyncMap提出了一种学习复杂结构的方法，并引入记忆窗口的概念来允许更多正向更新。在实验证明了其在结构学习问题中的出色表现，揭示了存在于时间数据中的拓扑结构和层次编码。

不均匀普遍分块问题的对称SyncMap