Skip to main content
SpringerLink
Log in

Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI

  • Chapter
  • First Online:
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 7))

Abstract

This chapter presents the challenges to information sciences when dealing with complex evolving processes in time-space. The emphasis here is on processes/systems that evolve/develop/unfold/change in time-space and what characterises them. To model such processes, to extract deep knowledge that drives them and to trace how this knowledge changes over time, are among the main objectives of the brain-like approach that we take in this book by using SNN. And before going to SNN in the next chapters, we introduce how evolving processes can be represented as data, information and knowledge, and more specifically, what is deep knowledge that we will target to achieve through deep learning in SNN.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover 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. Stephen Hawking, A Brief History of Time (Bantam Books, NY, 1990)

    Google Scholar 

  2. A. Einstein, Ideas and Opinions (Wings Books, NY, 1954)

    Google Scholar 

  3. C. Darwin, On the Origin of Species by Means of Natural Selection, or the Preservation of Favored Races in the Struggle for Life, 1st edn. (John Murray, London, 1859), p. 502

    Google Scholar 

  4. C. Darwin, On the Origin of Species by Means of Natural Selection, or the Preservation of Favored Races in the Struggle for Life, 2nd edn. (John Murray, London, 1860)

    Google Scholar 

  5. J.H. Holland, Adaptation in Natural and Artificial Systems, 2nd edn. (MIT Press, Cambridge, MA, 1992) (1st edn., University of Michigan Press, 1975)

    Google Scholar 

  6. N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering (MIT Press, Cambridge, MA, USA, 1996)

    MATH  Google Scholar 

  7. G. Carpenter, S. Grossberg, Adaptive Resonance Theory (MIT Press, 2017)

    Google Scholar 

  8. R. Penrose, The Emperors New Mind (Oxford University Press, 1989)

    Google Scholar 

  9. N. Kasabov (ed.), Springer Handbook of Bio-/Neuroinformatics (Springer, 2014)

    Google Scholar 

  10. R. Dawkins, The Selfish Gene (Oxford University Press, 1989)

    Google Scholar 

  11. D. Hebb, The Organization of Behavior (Wiley, New York, 1949)

    Google Scholar 

  12. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley, 1989)

    Google Scholar 

  13. Abraham et al., Insect biochem. Mol. Biol. 23(8), 905–912 (1993)

    Google Scholar 

  14. J. Gleick, Chaos Making A New Science (Viking Books, USA, 1987), p. 1987

    MATH  Google Scholar 

  15. O. Barndorff-Nielsen et al., Chaos and Networks: Statistical and Probabilistic Aspects (Chapman & Hall, London, New York, 1993), p. 1993

    Book  Google Scholar 

  16. F.C. Hoppensteadt, Intermittent chaos, self organizing and learning from synchronous synaptic activity in model neuron networks. Proc. Natl. Acad. Sci. USA 86, 2991–2995 (1989)

    Article  Google Scholar 

  17. Protein Database, https://www.ncbi.nlm.nih.gov/protein

  18. J. Ferguson, Aristotle (Twayne Publishers, New York, 1972)

    Google Scholar 

  19. J. De Groot, Aristotle’s Empiricism: Experience and Mechanics in the 4th Century BC (Parmenides Publishing, 2014). ISBN 978-1-930972-83-4

    Google Scholar 

  20. L.A. Zadeh, Fuzzy sets. Inf. Control 3(8), 338–353 (1965)

    Article  Google Scholar 

  21. L.A. Zadeh, Fuzzy logic. IEEE Comput. 4(21), 83–93 (1988)

    Google Scholar 

  22. B. Chandrasekaran, S. Mittal, Deep versus compiled knowledge approaches to diagnostic problem-solving. Int. J. Man Mach. Stud. 19(2), 425–436 (1983). https://doi.org/10.1016/S0020-7373(83)80064-9

    Article  Google Scholar 

  23. P.L. Rogers, G.A. Berg, J.V. Boettcher, C. Howard, L. Justice, K.D. Schenk, Encyclopedia of Distance Learning, vol. 4 (IGI Global, 2009)

    Google Scholar 

  24. A. Bennet, D. Bennet, Knowledge-Based Development for Cities and Societies: Integrated Multi-Level Approaches (IGI Global, 2010), https://doi.org/10.4018/978-1-61520-721-3.ch009

  25. L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–285 (1989)

    Article  Google Scholar 

  26. V.N. Vapnik, Statistical Learning Theory (Wiley, New York, 1998), p. 1998

    MATH  Google Scholar 

  27. V. Cherkassky, F. Mulier, Learning from Data: Concepts, Theory, and Methods (Wiley, New York, 1998), p. 1998

    MATH  Google Scholar 

  28. N. Kasabov, Adaptation and Interaction in dynamical systems: modelling and rule discovery through evolving connectionist systems. Appl. Soft Comput. 6(3), 307–322 (2006)

    Article  Google Scholar 

  29. Q. Song, N. Kasabov, NFI: a neuro-fuzzy inference method for transductive reasoning. IEEE Trans. Fuzzy Syst. 13(6), 799–808 (2005)

    Article  Google Scholar 

  30. N. Kasabov (2007) Evolving Connectionist Systems: The Knowledge Engineering Approach, 2nd ed. (Springer, London, 2007)

    Google Scholar 

  31. D. Dennett, From Bacteria to Bach and Back. The Evolution of Minds (W. W. Norton & Co., 2017)

    Google Scholar 

  32. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, 3rd edn. (Upper Saddle River, NJ, Prentice Hall, 2009)

    MATH  Google Scholar 

  33. N.J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge University Press, Cambridge, UK, 2010)

    Google Scholar 

  34. P. McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd edn. (Natick, MA, 2004)

    Google Scholar 

  35. N. Bostrom, V.C. Millar, Future progress in artificial intelligence: a survey of expert opinion, in Fundamental Issues of Artificial Intelligence, ed. by V.C. Muller (Springer, Synthese Library, Berlin, 2016), p. 553

    Google Scholar 

  36. J.H. Andreae (ed.), Man-Machine Studies. ISSN 0110 1188, nos. UC-DSE/4 and 5 (1974)

    Google Scholar 

  37. J.H. Andreae: Thinking with the Teachable Machine (Academic Press, 1977)

    Google Scholar 

  38. R.S. Sutton, A.G. Barto, Reinforcement Learning (MIT Press, Barto, 1998)

    Google Scholar 

  39. A. Clark, Surfing Uncertainty (Oxford University Press, 2016)

    Google Scholar 

  40. M. Minski, Steps toward artificial intelligence. Proc. IRE 49, 8–30 (1961)

    Google Scholar 

  41. The New Zealand AI Forum, http://aiforum.org.nz

  42. The AUT AI Initiative, http://www.aut.ac.nz/aii

  43. Wikipedia, http://www.wikipedia.org

Download references

Acknowledgements

Some of the material in this chapter was first published by the author in [6, 9, 30].

Author information

Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand

    Nikola K. Kasabov

Authors
  1. Nikola K. Kasabov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikola K. Kasabov .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kasabov, N.K. (2019). Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_1

Download citation

Publish with us

Policies and ethics

Search

Navigation