自己組織化による学習：人工知能の新たな基礎を開拓

在这项研究中，我们进一步发展了新的机器学习范式，仅基于动力学方程，没有优化。SyncMap在稳定性（对称SyncMap）、处理高维（Magnum）以及处理不断变化的层次结构（Sigma）方面得到了进一步的改进。对称SyncMap改进了SyncMap方程，解决了初始方程的长期稳定性问题。Magnum在变量子集中使用多个惯性同步映射，使其能够处理高维问题。Sigma取得了惊人的成就，它既能准确地预测分层输入结构的结构，又能在整个实验过程中适应分层输入结构的变化。所有这些方法都经过了广泛的测试，其结果被提交给该领域的顶级期刊。希望它们中的大多数将在未来几个月内可用。我们还对如何使用输入序列来处理图像和其他非顺序输入有了两个关键的理解。基于这些新发现，我们开发了一种新的计算机视觉架构和两种用于监督学习分类任务的方法。

In this research we further developed the novel machine learning paradigm based solely on dynamical equations and no optimization. SyncMap was further improved in stability (Symmetrical SyncMap), to deal with high dimensionality (Magnum) and also to work with changing hierarchies (Sigma). Symmetrical SyncMap improves the equations of SyncMap to deal with long-term stability of the initial equations. Magnum uses multiple Inertia-SyncMaps in subsets of variables, enabling it to deal with high dimensional problems. Sigma achieves the astonishing feat of both being accurate enough to predict the structure of hierarchical input structures at the same time as being adaptive enough to deal with changes in them throughout the experiments. All these methods were extensively tested and their results were submitted to top journals in the area. Hopefully, most of them will become available in the next few months.We also developed two key understanding of how input sequences can be used generally to deal with images and other non-sequential input. Based on these new findings, we have proceeded to develop a novel architecture for computer vision and two methodologies for supervised learning classification tasks.