Abstract
In order to understand human intelligence in depth and find the cognitive models needed by Web Intelligence (WI), Brain Informatics (BI) adopts systematic methodology to study human “thinking centric” cognitive functions, and their neural structures and mechanisms in which the brain operates. For supporting systematic BI study, we propose a new conceptual brain data model, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS). On one hand, constructing such a Data-Brain is the requirement of systematic BI study. On the other hand, BI methodology supports such a Data-Brain construction. In this paper, we design a multi-dimension framework of Data-Brain and propose a BI methodology based approach for Data-Brain modeling. By this approach, we can construct a formal Data-Brain which provides a long-term, holistic vision to understand the principles and mechanisms of HIPS.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Chen, P.: The Entity-Relationship Model-towards a Unified View of Data. ACM Transactions on Database Systems 1(1), 9–36 (1976)
Chen, J.H., Zhong, N.: Data-Brain Modeling Based on Brain Informatics Methodology. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2008), pp. 41–47. IEEE Computer Society Press, Los Alamitos (2008)
Dameron, O., Gibaud, B., et al.: Towards a Sharable Numeric and Symbolic Knowledge Base on Cerebral Cortex Anatomy: Lessons from a Prototype. In: AMIA Symposium (AMIA 2002), pp. 185–189 (2002)
Fonseca, F., Martin, J.: Learning the Differences between Ontologies and Conceptual Schemes through Ontology-Driven Information Systems. JAIS, Special Issue on Ontologies in the Context of IS 8(2), 129–142 (2007)
Jarrar, M., Demey, J., Meersman, R.: On Using Conceptual Data Modeling for Ontology Engineering. In: Aberer, K., March, S., Spaccapietra, S. (eds.) Journal of Data Semantics. LNCS, vol. 2800, pp. 185–207. Springer, Heidelberg (2003)
Jin, H., Sun, A., et al.: Ontology-based Semantic Integration Scheme for Medical Image Grid. International Journal of Grid and Utility Computing 1(2), 86–97 (2009)
Noy, N.F., Musen, M.A.: Specifying Ontology Views by Traversal. In: McIlraith, S.A., et al. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 713–725. Springer, Heidelberg (2004)
Uschold, M., King, M.: Towards Methodology for Building Ontologies. In: Workshop on Basic Ontological Issues in Knowledge Sharing, held in conjunction with IJCAI 1995 (1995)
Uschold, M., Gruninger, M.: Ontologies Principles, Methods and Applications. Knowledge Engineering Review 11(2), 93–155 (1996)
Wang, Y.X., Wang, Y., et al.: A Layered Reference Model of the Brain (LRMB). IEEE Transactions on Systems, Man, and Cybernetics (C) 36, 124–133 (2006)
Zhong, N., Liu, J., Yao, Y.Y.: In Search of the Wisdom Web. IEEE Computer 35(11), 27–31 (2002)
Zhong, N.: Building a Brain-Informatics Portal on the Wisdom Web with a Multi-layer Grid: A New Challenge for Web Intelligence Research. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds.) MDAI 2005. LNCS (LNAI), vol. 3558, pp. 24–35. Springer, Heidelberg (2005)
Zhong, N.: Impending Brain Informatics (BI) Research from Web Intelligence (WI) Perspective. International Journal of Information Technology and Decision Making 5(4), 713–727 (2006)
Zhong, N.: Actionable Knowledge Discovery: A Brain Informatics Perspective. Special Trends and Controversies department on Domain-Driven, Actionable Knowledge Discovery, IEEE Intelligent Systems, 85–86 (2007)
Australian EEG Database, http://eeg.newcastle.edu.au/inquiry/
Simulated Brain Database, http://www.bic.mni.mcgill.ca/brainweb/
Brain Bank, http://www.brainbank.cn/
The fMRI Data Center, http://www.fmridc.org/f/fmridc
Olfactory Receptor DataBase, http://senselab.med.yale.edu/ORDB/default.asp
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, J., Zhong, N. (2009). Data-Brain Modeling for Systematic Brain Informatics. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds) Brain Informatics. BI 2009. Lecture Notes in Computer Science(), vol 5819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04954-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-04954-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04953-8
Online ISBN: 978-3-642-04954-5
eBook Packages: Computer ScienceComputer Science (R0)