Advertisement

Brain Big Data in Wisdom Web of Things

  • Ning ZhongEmail author
  • Stephen S. Yau
  • Jianhua Ma
  • Shinsuke Shimojo
  • Marcel Just
  • Bin Hu
  • Guoyin Wang
  • Kazuhiro Oiwa
  • Yuichiro Anzai
Chapter
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)

Abstract

The chapter summarizes main aspects of brain informatics based big data interacting with a social-cyber-physical space of Wisdom Web of Things (W2T). It describes how to realize human-level collective intelligence as a big data sharing mind—a harmonized collectivity of consciousness on the W2T by developing brain inspired intelligent technologies to provide wisdom services, and it proposes five guiding principles to deeper understand the nature of the vigorous interaction and interdependence of brain-body-environment.

Keywords

Information Communication Technology Link Open Data Vibration Isolation System Brain Activity Measurement Wearable Health 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by grants from the National Basic Research Program of China (2014CB744600), the International Science & Technology Cooperation Program of China (2013DFA32180), the National Natural Science Foundation of China (61420106005 and 61272345), the Beijing Natural Science Foundation (4132023), and the JSPS Grants-in-Aid for Scientific Research of Japan (26350994).

References

  1. 1.
    N. Zhong, J.H. Ma, R.H. Huang, J.M. Liu, Y.Y. Yao, Y.X. Zhang, J.H. Chen, Research challenges and perspectives on wisdom Web of things (W2T). The Journal of Supercomputing 64(3), 862882 (2013)CrossRefGoogle Scholar
  2. 2.
    N. Zhong, J.M. Bradshaw, J. Liu, J.G. Taylor, Brain informatics. IEEE Intelligent Systems 26(5), 16–21 (2011)CrossRefGoogle Scholar
  3. 3.
    J. Chen, J.H. Ma, N. Zhong, Y.Y. Yao, J. Liu, R.H. Huang, W. Li, Z. Huang, Y. Gao, J. Cao, WaaS—Wisdom as a service. IEEE Intelligent Systems 29(6), 40–47 (2014)CrossRefGoogle Scholar
  4. 4.
    D. Douglas, The limits of intelligence. Scientific American 37–43, (July 2011)Google Scholar
  5. 5.
    F. Heylighen, The global superorganism: an evolutionary-cybernetic model of the emerging network society. Social Evolution & History 6(1), 58119 (2007)Google Scholar
  6. 6.
    T. Murata, N. Matsui, S. Miyauchi, Y. Kakita, T. Yanagida, Discrete stochastic process underlying perceptual rivalry. NeuroReport 14, 1347–1352 (2003)CrossRefGoogle Scholar
  7. 7.
    O. Sporns, Making sense of brain network data. Nature Methods 10(6), 491–493 (2013)CrossRefGoogle Scholar
  8. 8.
    H.-J. Park, Karl Friston, Structural and functional brain networks: From connections to cognition. Science 342, 1238411 (2013)CrossRefGoogle Scholar
  9. 9.
    T. Horikawa, M. Tamaki, Y. Miyawaki, Y. Kamitani, Neural decoding of visual imagery during sleep. Science 340, 639–642 (2013)CrossRefGoogle Scholar
  10. 10.
    T. Cukur, S. Nishimoto, A.G. Huth, J.L. Gallant, Attention during natural vision warps semantic representation across the human brain. Nature Neuroscience 16, 763–770 (2013)CrossRefGoogle Scholar
  11. 11.
    V.K. Lee, L.T. Harris, How social cognition can inform social decision making. Front Neuroscience. (2013). doi: 10.3389/fnins Google Scholar
  12. 12.
    M. Haruno, C. Frith, Activity in the amygdala elicited by unfair divisions predicts social value orientation. Nature Neuroscience 13, 160–161 (2013)CrossRefGoogle Scholar
  13. 13.
    N. Turk-Browne, Functional interactions as big data in the human brain. Science 342, 580–584 (2013)CrossRefGoogle Scholar
  14. 14.
    T.M. Mitchell, S.V. Shinkareva, A. Carlson, K.M. Chang, V.L. Malave, R.A. Mason, M.A. Just, Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008)CrossRefGoogle Scholar
  15. 15.
    T.A. Keller, M.A. Just, Altering cortical connectivity: Remediation-induced changes in the white matter of poor readers. Neuron 64, 624–631 (2009)CrossRefGoogle Scholar
  16. 16.
    S. Nishimoto, A. T. Vu, T. Naselaris, Y. Benjamini, B. Yu, J. L. Gallant. Reconstructing visual experiences from brain activity evoked by natural movies. Current Biology 21, 1641–1646 (2011)CrossRefGoogle Scholar
  17. 17.
    B. Hu, D. Majoe, M. Ratcliffe, Y. Qi, Q. Zhao, H. Peng, D. Fan, F. Zheng, M. Jackson, P. Moore, EEG-based cognitive interfaces for ubiquitous applications: developments and challenges. IEEE Intelligent Systems 26(5), 46–53 (2011)CrossRefGoogle Scholar
  18. 18.
    D. Fensel, F. van Harmelen, B. Andersson, P. Brennan, H. Cunningham, E.D. Valle, F. Fischer, Z.S. Huang, A. Kiryakov, T.K.-I. Lee, L. Schooler, V. Tresp, S. Wesner, M. Witbrock, N. Zhong, Towards LarKC: a platform for Web-scale reasoning. Proc. ICSC 524–529, 2008 (2008)Google Scholar
  19. 19.
    N. Zhong, J. Chen, Constructing a new-style conceptual model of brain data for systematic brain informatics. IEEE Transactions on Knowledge and Data Engineering 24(12), 2127–2142 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    G.Y. Wang, J. Xu, Granular computing with multiple granular layers for brain big data processing. Brain Informatics (2014). doi: 10.1007/s40708-014-0001-z Google Scholar
  21. 21.
    G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Y. Anzai, Human-robot interaction by information sharing. Proc. HRI 65–66, 2013 (2013)Google Scholar
  23. 23.
    J.H. Ma, J. Wen, R.H. Huang, B.X. Huang, Cyber-individual meets brain informatics. IEEE Intelligent Systems 26(5), 30–37 (2011)CrossRefGoogle Scholar
  24. 24.
    S. Shimojo, C. Simion, E. Shimojo, C. Scheier, Gaze bias both reflects and influences preference. Nature Neuroscience 6, 1317 (2003)CrossRefGoogle Scholar
  25. 25.
    I. Murakami, A. Kitaoka, H. Ashida, A positive correlation between fixation instability and the strength of illusory motion in a static display. Vision Research 46, 24212431 (2006)CrossRefGoogle Scholar
  26. 26.
    G. Ishimura and S. Shimojo. Voluntary action captures visual motion. Investigative Ophthalmology and Visual Science (Suppl.), 35: 1275, 1994Google Scholar
  27. 27.
    J.P. Lindsen, R. Jones, S. Shimojo, J. Bhattachary, Neural components underlying subjective preferential decision making. NeuroImage 50, 16261632 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ning Zhong
    • 1
    • 2
    Email author
  • Stephen S. Yau
    • 3
  • Jianhua Ma
    • 4
  • Shinsuke Shimojo
    • 5
  • Marcel Just
    • 6
  • Bin Hu
    • 7
  • Guoyin Wang
    • 8
  • Kazuhiro Oiwa
    • 9
  • Yuichiro Anzai
    • 10
  1. 1.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan
  2. 2.International WIC InstituteBeijing University of TechnologyBeijingChina
  3. 3.Arizona State UniversityTempeUSA
  4. 4.Faculty of Computer and Information ScienceHosei UniversityTokyoJapan
  5. 5.California Institute of TechnologyPasadenaUSA
  6. 6.Carnegie Mellon UniversityPittsburghUSA
  7. 7.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  8. 8.Chongqing University of Posts and TelecommunicationsChongqingChina
  9. 9.National Institute of Information and Communication TechnologyKoganeiJapan
  10. 10.Japan Society for the Promotion of ScienceTokyoJapan

Personalised recommendations