THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing
THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing
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DOI：
10.1109/access.2019.2963070
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Hongzhi Kuai;Xiaofei Zhang;Yang Yang-Yang;Jianhui Chen;Bin Shi;Ning Zhong
中科院分区：
文献类型：
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作者：
Hongzhi Kuai;Xiaofei Zhang;Yang Yang-Yang;Jianhui Chen;Bin Shi;Ning Zhong
高复杂性意味着一个模型的组成部分以多种方式相互作用，并遵循一定的局部规则，这对大脑研究来说是一个巨大的挑战。本文提出了一种语义向量驱动的脑计算闭环模型THINKING-LOOP，以提高对复杂认知的理解和发展。该模型利用本体知识建模、基于规则的推理和人机交互机制，实现了数据、信息和知识与人类智能的三层融合。模型内部的交互与协作依赖于一对互补的循环方案：从知识层到数据层的自顶向下方案，用于寻找稳定的认知模式；从数据层到知识层的自底向上方案，用于深入分析认知功能。人作为一个关键因素，参与到模型的整个学习过程中，从而帮助人类做出决策。为了验证本模型在认知研究中的适用性，进行了一系列fMRI实验和分析方法（如统计测试和网络拓扑分析）。结果表明，该模型能够兼顾不同类型脑模式和认知功能的特点，从而达到合理的决策水平。
High complexity, meaning a model in which components interact in multiple ways and follow certain local rules, is a huge challenge for brain research. This paper presents a semantic vector-driven closed-loop model, namely THINKING-LOOP, for brain computing to improve the understanding and development of complex cognition. The proposed model is a three-layer fusion of data, information and knowledge with human intelligence, which exploits ontological knowledge modeling, rule-based reasoning and a human-computer interaction mechanism. The interaction and collaboration within the model depend on a pair of complementary schemes in a loop: the top-down scheme from the knowledge layer to the data layer that is used to search for stable cognitive patterns; and the bottom-up scheme from the data layer to the knowledge layer that is used to deeply analyze cognitive functions. As a key factor, human beings participate in the whole learning process of the model, which in turn assists human beings to make decisions. To verify the applicability of the present model in cognitive research, a series of fMRI experiments and analytic methods (e.g. statistical tests and network topology analysis) were conducted. The results show that the proposed model is able to take into account the characteristics of different types of brain patterns and cognitive functions, thereby achieving reasonable decision-making level.