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Brain Research and Arbitrary Multiscale Quantum Uncertainty

  • Rodolfo A. FioriniEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1072)

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.

Keywords

Knowledge representation Neuroscience Cognition and behavior Machine learning Modeling Simulation CICT Deep learning 

Notes

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.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.DEIBPolitecnico di Milano UniversityMilanItaly

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