Kempitiya Thimal, Alahakoon Damminda, Osipov Evgeny, Kahawala Sachin, De Silva Daswin
Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia.
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden.
Biomimetics (Basel). 2024 Mar 13;9(3):175. doi: 10.3390/biomimetics9030175.
We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm.
我们提出了一种受自然科学和神经科学启发的新算法，用于基于顺序回忆和向量符号架构的时空学习与预测。一个关键的新颖之处在于，将空间和时间模式作为解耦的概念进行学习，其中时间模式序列是使用所学的空间模式作为元素字母表构建而成的。这种解耦是受认知神经科学研究的启发，为在数据动态变化和概念漂移的情况下进行快速自适应学习提供了灵活性，因此更适合实时学习和预测。该算法进一步解决了基于现实生活中的时空数据预测下一次出现情况的几个关键计算要求，而这些要求已被发现对当前的先进算法具有挑战性。首先，在不断变化的环境中，使用无监督学习从未标记的数据流中检测空间和时间模式；其次，使用向量符号架构（VSA）来管理可变长度序列；第三，基于超维（HD）计算的关联记忆用于促进对顺序模式中下一次出现情况的连续预测。该算法已通过使用两个基准数据集和三个时间序列数据集进行了实证评估，以证明其在时空无监督序列学习方面相对于现有技术的优势，其中所提出的ST-SOM算法与HTM算法相比能够将误差降低45%。
