Skip to main content
SpringerLink
Log in

Brain Research and Arbitrary Multiscale Quantum Uncertainty

  • Conference paper
  • First Online:
Human Brain and Artificial Intelligence (HBAI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1072))

Included in the following conference series:

Abstract

The fact that we can build devices that implement the same basic operations as those the nervous system uses leads to the inevitable conclusion that we should be able to build entire systems based on the network organizing principles used by the nervous system. Nevertheless, the human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine. The unavoidable conclusion is that we still have something fundamental to learn from the brain and neurobiology about new ways and much more effective forms of computation. To acquire new knowledge on multiscale system uncertainty management, three specific interpretations of the Heisenberg Uncertainty Principle are presented and discussed, even at macroscale level. To solve complex, arbitrary multiscale system problems, by advanced deep learning and deep thinking systems, we need a unified, integrated, convenient, and universal representation framework, by considering information not only on the statistical manifold of model states, but also on the combinatorical manifold of low-level discrete, directed energy generators. Understanding this deep layer of thought is vital to develop highly competitive, reliable and effective human-centered symbiotic autonomous systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fiorini, R.A.: More effective biomedical experimentation data by CICT advanced ontological uncertainty management techniques. Int. J. Biol. Biomed. Eng. 9, 29–41 (2015)

    Google Scholar 

  2. Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, 245–251 (2005). https://doi.org/10.1371/journal.pcbi.0010042

    Article  Google Scholar 

  3. Deisseroth, K.: Optogenetics. Nat. Methods Comment. (2010). https://doi.org/10.1038/NMETH.F.324, https://web.stanford.edu/group/dlab/media/papers/deisserothnature2010.pdf

    Article  Google Scholar 

  4. Human Connectome Project. http://www.humanconnectomeproject.org/

  5. Human Brain Project. https://www.humanbrainproject.eu/en/

  6. Markram, H., Muller, E., Ramaswamy, S., Hill, S.L., Segev, I., Schürmann, F., et al.: Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492 (2015)

    Article  Google Scholar 

  7. Horvitz, H.R.: Worms, life, and death (Nobel lecture). ChemBioChem 4, 697–711 (2003)

    Article  Google Scholar 

  8. Shapshak, P.: Artificial intelligence and brain. Bioinformation 14(1), 38–41 (2018)

    Article  Google Scholar 

  9. Resconi, G.: Geometry of Knowledge for Intelligent Systems. Studies on Computational Intelligence, vol. 407. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-27972-0

    Book  MATH  Google Scholar 

  10. Azevedo, F.A.C., et al.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513(5), 532–541 (2009)

    Article  Google Scholar 

  11. Brotherson, S.: Understanding brain development in young children, FS-609. n.d. NDSU Extension Service, April 2009. http://www.ag.ndsu.edu/pubs/yf/famsci/fs609w.htm

  12. Haueis, P.: Meeting the brain on its own terms. Front. Hum. Neurosci. (2014). http://journal.frontiersin.org/article/10.3389/fnhum.2014.00815/full

  13. Biswal, B.B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S.M.: Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A. 107, 4734–4739 (2010). https://doi.org/10.1073/pnas.0911855107

    Article  Google Scholar 

  14. Sporns, O.: Networks of the Brain. MIT Press, Cambridge (2011)

    MATH  Google Scholar 

  15. Sporns, O.: Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17, 652–660 (2014). https://doi.org/10.1038/nn.3690

    Article  Google Scholar 

  16. Hagmann, P., et al.: Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), e159, 1479–1493 (2008)

    Article  Google Scholar 

  17. Zorzos, A.N., Scholvin, J., Boyden, E.S., Fonstad, C.G.: Three-dimensional multiwaveguide probe array for light delivery to distributed brain circuits. Opt. Lett. 37(23), 4841–4843 (2012)

    Article  Google Scholar 

  18. Cruz, L., et al.: A statistically based density map method for identification and quantification of regional differences in microcolumnarity in the monkey brain. J. Neurosci. Methods 141(2), 321–332 (2005)

    Article  Google Scholar 

  19. Buxhoeveden, D.P., Casanova, M.F.: The minicolumn hypothesis in neuroscience. Brain 125(5), 935–951 (2002)

    Article  Google Scholar 

  20. Buldirev, S.V., Cruz, L., Gomez-Isla, T., Gomez-Tortosa, E., Havlin, S., Le, R.: Descrimination of microcolumnar ensembles in association cortex and their disruption in Alzheimer and Lewy body dementias. Proc. Natl. Acad. Sci. U.S.A. 97, 5039–5043 (2000)

    Article  Google Scholar 

  21. Blagoev, K.B., et al.: Modelling the magnetic signature of neuronal tissue. Neuroimage 37, 137–148 (2007)

    Article  Google Scholar 

  22. Howard, N., Hussain, A.: The fundamental code unit of the brain: towards a new model for cognitive geometry. Cogn. Comput. 10(3), 426–436 (2018)

    Article  Google Scholar 

  23. Fiorini, R.A.: From computing with numbers to computing with words. In: Howard, N., Wang, Y., Hussain, A., Hamdy, F., Widrow, B., Zadeh, L.A. (eds.) Proceedings of the IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, pp. 84–91. IEEE Press, New York (2017)

    Google Scholar 

  24. Tononi, G., Boly, M., Massimini, M., Koch, C.: Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci. 17(7), 450–461 (2016)

    Article  Google Scholar 

  25. Fiorini, R.A.: How random is your tomographic noise? A number theoretic transform (NTT) approach. Fundam. Infomaticae 135(1–2), 135–170 (2014)

    MathSciNet  MATH  Google Scholar 

  26. Wang, Y., et al.: Cognitive intelligence: deep learning, thinking, and reasoning by brain-inspired systems. Int. J. Cogn. Inform. Nat. Intell. 10(4), 1–21 (2016)

    Article  Google Scholar 

  27. Fiorini, R.A.: Brain-inspired systems and predicative competence. In: Howard, N., Wang, Y., Hussain, A., Hamdy, F., Widrow, B., Zadeh, L.A. (eds.) Proceedings of the IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, pp. 268–275. IEEE Press, New York (2017)

    Google Scholar 

  28. Fiorini, R.A.: A cybernetics update for competitive deep learning system. In: Proceedings of the 2nd International Electronic Conference on Entropy and Its Applications. MDPI, 15–30 November 2015. http://sciforum.net/conference/ecea-2/paper/3277

  29. Fiorini, R.A.: Human-centered symbiotic system science. In: Soda, P., et al. (eds.) Proceedings of the IEEE ICCI*CC 2019, 18th International Conference on Cognitive Informatics and Cognitive Computing, pp. 286–292. IEEE Press, New York (2019)

    Google Scholar 

  30. LeDoux, J.: The Emotional Brain: The Mysterious Underpinnings of Emotional Life. Weidenfeld & Nicolson, Great Britain (1998)

    Google Scholar 

  31. LeDoux, J.: Synaptic Self: How Our Brains Become Who We Are. Viking Penguin, New York (2002)

    Google Scholar 

  32. Fiorini, R.A., Santacroce, G.F.: Economic competitivity in healthcare safety management by biomedical cybernetics ALS. In: 2013 Proceedings International Symposium, The Economic Crisis: Time for a Paradigm Shift - Towards a Systems Approach, pp. 24–25. Universitat de València, Valencia (2013)

    Google Scholar 

  33. Nicolescu, B.: Levels of complexity and levels of reality. In: Pullman, B. (ed.) Proceedings of the Plenary Session of the Pontifical Academy of Sciences on the Emergence of Complexity in Mathematics, Physics, Chemistry, and Biology. Casina Pio IV, Vatican, Pontifical Academy of Sciences, Vatican City, 27–31 October 1992. Princeton University Press, Princeton (1992)

    Google Scholar 

  34. Fiorini, R.A.: Evolutive information in the anthropocene era. In: Dodig-Crnkovic, G., Burgin, M. (eds.) Philosophy and Methodology of Information, Part 2. Methodology of Information, pp. 201–261. World Scientific, Singapore (2019)

    Chapter  Google Scholar 

  35. Fiorini, R.A.: From epistemic uncertainty quantification to ontological uncertainty management for system safety and security. In: Proceedings of the IEEE 1st International Forum on Research and Technologies for Society and Industry: Leveraging a Better Tomorrow, Torino, Italy, 16–18 September 2015, pp. 312–319. IEEE Press, Torino (2015)

    Google Scholar 

  36. Fiorini, R.A.: Embracing the unknown in intelligent systems. In: Proceedings of the 18th International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems, MAMETICS 2016, Venice, Italy, 29–31 January 2016. WSEAS Press, Venice (2016)

    Google Scholar 

  37. Einstein, A.: Über die elektromagnetischen Grundgleichungen für bewegte Körper. Ann. der Phys. 331, 532–540 (1908)

    Article  Google Scholar 

  38. Hestenes, D.: Space-Time Algebra. Gordon and Breach, New York (1966)

    MATH  Google Scholar 

  39. Cassidy, D.C.: Uncertainty: The Life and Science of Werner Heisenberg. W.H. Freeman and Company, New York (1992)

    Google Scholar 

  40. Fiorini, R.A.: Computerized tomography noise reduction by CICT optimized exponential cyclic sequences (OECS) co-domain. Fundam. Inform. 141, 115–134 (2015)

    Article  MathSciNet  Google Scholar 

  41. Fiorini, R.A., Laguteta, G.: Discrete tomography data footprint reduction by information conservation. Fundam. Inform. 125(3–4), 261–272 (2013)

    MathSciNet  Google Scholar 

  42. Laszlo, E.: Science and the Akashic Field: An Integral Theory of Everything. Amazon Media EU S.à r.l. (2010)

    Google Scholar 

  43. Laszlo, E., Tobias, M.: The Tuscany Dialogues: The Earth, Our Future, and the Scope of Human Consciousness. SelectBooks, Incorporated, New York (2018)

    Google Scholar 

  44. Di Corpo, U., Vannini, A.: Syntropy: The Spirit of Love. ICRL Press, Princeton (2015)

    Google Scholar 

  45. Bono, I., Del Giudice, E., Gamberale, L., Henry, M.: Emergence of the coherent structure of liquid water. Water 4(3), 510–532 (2012). https://doi.org/10.3390/w4030510

    Article  Google Scholar 

  46. Preparata, G.: QED Coherence in Matter. World Scientific, Singapore (1995)

    Book  Google Scholar 

  47. Del Giudice, E., Vitiello, G.: Role of the electromagnetic field in the formation of domains in the process of symmetry breaking phase transitions. Phys. Rev. A 74(2), 1–9 (2006). Article ID 022105

    Google Scholar 

  48. Lamb, W.E., Retherford, R.C.: Fine structure of the hydrogen atom by a microwave method. Phys. Rev. 72(3), 241–243 (1947)

    Article  Google Scholar 

  49. Casimir, H.B.G.: On the attraction between two perfectly conducting plates. Proc. K. Ned. Akademie VanWetenschappen B 51, 793–796 (1948)

    MATH  Google Scholar 

  50. Gurwitsch, A.A.: A historical review of the problem of mitogenetic radiation. Experientia 44, 545–550 (1988)

    Article  Google Scholar 

  51. Beloussov, L.V., Opitz, J.M., Gilbert, S.F.: Life of Alexander G. Gurwitsch and his relevant contribution to the theory of morphogenetic fields. Int. J. Dev. Biol. 41, 771–779 (1997)

    Google Scholar 

  52. Fiorini, R.A.: Strumentazione Biomedica: Sistemi di Supporto Attivo. CUSL, Collana Scientifica, Milano (1994)

    Google Scholar 

  53. De Giacomo, P., Fiorini, R.A.: Creativity mind. Amazon (2019)

    Google Scholar 

  54. Fiorini, R.A.: From autonomous systems to symbiotic system science. In: Soda, P., et al. (eds.) Proceedings of the IEEE ICCI*CC 2019, 18th International Conference on Cognitive Informatics and Cognitive Computing, pp. 254–260. IEEE Press, New York (2019)

    Google Scholar 

  55. Fiorini, R.A.: Arbitrary multiscale explainable decision-making for symbiotic autonomous systems. Keynote speech. In: IEEE ICCI*CC 2019, 18th International Conference on Cognitive Informatics and Cognitive Computing. Politecnico di Milano, Milano, Italy, 23–25 July 2019, p. 6. IEEE Press, New York (2019)

    Google Scholar 

  56. Wang, Y., et al.: On autonomous systems: from reflexive, imperative and adaptive intelligence to autonomous and cognitive intelligence. In: Soda, P., et al. (eds.) Proceedings of the IEEE ICCI*CC 2019, 18th International Conference on Cognitive Informatics and Cognitive Computing, pp. 7–12. IEEE Press, New York (2019)

    Google Scholar 

  57. Lützen, J.: Joseph Liouville 1809–1882 – Master of Pure and Applied Mathematics. Springer, Heidelberg (1990). https://doi.org/10.1007/978-1-4612-0989-8

    Book  MATH  Google Scholar 

  58. Taleb, N.N., Goldstein, D.G.: The problem is beyond psychology: the real world is more random than regression analyses. Int. J. Forecast. 28(3), 715–716 (2012)

    Article  Google Scholar 

  59. Taleb, N.N.: Silent Risk: Lectures on Probability, vol. 1, January 2015. https://drive.google.com/file/d/0B8nhAlfIk3QIR1o1dnk5ZmRaaGs/view?pli=1

Download references

Acknowledgements

Author acknowledges the continuous support from the CICT CORE Group of Politecnico di Milano University, Milano, Italy, for extensive computational modelling, simulation resources and enlightening talks. Furthermore, the author is grateful to anonymous reviewers for their perceptive and helpful comments, which helped the author substantially improve previous versions of the manuscript.

Author information

Authors and Affiliations

  1. DEIB, Politecnico di Milano University, 20133, Milan, Milan, Italy

    Rodolfo A. Fiorini

Authors
  1. Rodolfo A. Fiorini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rodolfo A. Fiorini .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    An Zeng

  2. Guangdong Construction Polytechnic, Guangzhou, China

    Dan Pan

  3. South China Normal University, Guangzhou, China

    Tianyong Hao

  4. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. University of Notre Dame, South Bend, IN, USA

    Yiyu Shi

  6. Simon Fraser University, Vancouver, BC, Canada

    Xiaowei Song

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fiorini, R.A. (2019). Brain Research and Arbitrary Multiscale Quantum Uncertainty. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1398-5_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1397-8

  • Online ISBN: 978-981-15-1398-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation