THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing
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 developmen...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8949532/ |
| _version_ | 1828977373837524992 |
|---|---|
| author | Hongzhi Kuai Xiaofei Zhang Yang Yang Jianhui Chen Bin Shi Ning Zhong |
| author_facet | Hongzhi Kuai Xiaofei Zhang Yang Yang Jianhui Chen Bin Shi Ning Zhong |
| author_sort | Hongzhi Kuai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T14:55:59Z |
| format | Article |
| id | doaj.art-7d3cd44e807d47bf9600ec4ab3884996 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:55:59Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7d3cd44e807d47bf9600ec4ab38849962022-12-21T22:56:58ZengIEEEIEEE Access2169-35362020-01-0184273428810.1109/ACCESS.2019.29630708949532THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain ComputingHongzhi Kuai0https://orcid.org/0000-0002-2746-648XXiaofei Zhang1https://orcid.org/0000-0002-3859-7709Yang Yang2https://orcid.org/0000-0001-5387-8640Jianhui Chen3https://orcid.org/0000-0001-6501-9819Bin Shi4https://orcid.org/0000-0002-0827-3543Ning Zhong5https://orcid.org/0000-0001-7882-8340Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, JapanInternational WIC Institute, Beijing University of Technology, Beijing, ChinaDepartment of Psychology, Beijing Forestry University, Beijing, ChinaInternational WIC Institute, Beijing University of Technology, Beijing, ChinaInternational WIC Institute, Beijing University of Technology, Beijing, ChinaDepartment of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, JapanHigh 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.https://ieeexplore.ieee.org/document/8949532/Expert systemshuman computer interactionbrain informaticsfMRIdata mining |
| spellingShingle | Hongzhi Kuai Xiaofei Zhang Yang Yang Jianhui Chen Bin Shi Ning Zhong THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing IEEE Access Expert systems human computer interaction brain informatics fMRI data mining |
| title | THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing |
| title_full | THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing |
| title_fullStr | THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing |
| title_full_unstemmed | THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing |
| title_short | THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing |
| title_sort | thinking loop the semantic vector driven closed loop model for brain computing |
| topic | Expert systems human computer interaction brain informatics fMRI data mining |
| url | https://ieeexplore.ieee.org/document/8949532/ |
| work_keys_str_mv | AT hongzhikuai thinkingloopthesemanticvectordrivenclosedloopmodelforbraincomputing AT xiaofeizhang thinkingloopthesemanticvectordrivenclosedloopmodelforbraincomputing AT yangyang thinkingloopthesemanticvectordrivenclosedloopmodelforbraincomputing AT jianhuichen thinkingloopthesemanticvectordrivenclosedloopmodelforbraincomputing AT binshi thinkingloopthesemanticvectordrivenclosedloopmodelforbraincomputing AT ningzhong thinkingloopthesemanticvectordrivenclosedloopmodelforbraincomputing |