Why deep neural nets cannot ever match biological intelligence and what to do about it?


The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artificial intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks.


  1. [1]

    D. Nikolić. Practopoiesis: Or how life fosters a mind. Journal of Theoretical Biology, vol. 373, pp. 40–61, 2015.

    Article  MATH  Google Scholar 

  2. [2]

    D. Nikolić. Practopoiesis: How cybernetics of biology can help AI. [Online], Availabe: https://www.singularityweblog.com/practopoiesis/, 2014.

    Google Scholar 

  3. [3]

    G. R. Chen. Pinning control and controllability of complex dynamical networks. International Journal of Automation and Computing, vol. 14, no. 1, pp. 1–9, 2017.

    Article  Google Scholar 

  4. [4]

    Y. Jiang, J. Y. Dai. An adaptive regulation problem and its application. International Journal of Automation and Computing, vol. 14, no. 2, pp. 221–228, 2017.

    Article  Google Scholar 

  5. [5]

    R. S. Sutton, A. G. Barto. Reinforcement Learning, Cambridge, Mass, USA: MIT Press, 1998.

    Google Scholar 

  6. [6]

    C. J. C. H. Watkins. Learning from Delayed Rewards, Ph.D. dissertation, Cambridge University, UK, 1989.

    Google Scholar 

  7. [7]

    W. R. Ashby. Principles of the self-organizing dynamic system. The Journal of General Psychology, vol. 37, no. 2, pp. 125–128, 1947.

    Article  Google Scholar 

  8. [8]

    R. C. Conant, W. R. Ashby. Every good regulator of a system must be a model of that system. International Journal of Systems Science, vol. 1, no. 2, pp. 89–97, 1970.

    MathSciNet  Article  MATH  Google Scholar 

  9. [9]

    T. M. Bartol, C. Bromer, J. P. Kinney, M. A. Chirillo, J. N. Bourne, K. M. Harris, T. J. Sejnowski. Hippocampal spine head sizes are highly precise. bioRxiv, [Online], Available: http://dx.doi.org/10.1101/016329, March 11, 2015.

    Google Scholar 

  10. [10]

    S. Corkin. Lasting consequences of bilateral medial temporal lobectomy: Clinical course and experimental findings in H.M. Seminars in Neurology, vol. 4, no. 2, pp. 249–259, 1984.

    Article  Google Scholar 

  11. [11]

    A. M. Treisman, G. Gelade. A feature-integration theory of attention. Cognitive Psychology, vol. 12, no. 1, pp. 97–136, 1980.

    Article  Google Scholar 

  12. [12]

    A. Treisman. Preattentive processing in vision. Computer Vision, Graphics, and Image Processing, vol. 31, no. 2, pp. 156–177, 1985.

    MathSciNet  Article  Google Scholar 

  13. [13]

    G. A. Miller. The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review, vol. 63, no. 2, pp. 81–97, 1956.

    Article  Google Scholar 

  14. [14]

    N. Cowan. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, vol. 24, no. 1, pp. 87–114, 2001.

    Article  Google Scholar 

  15. [15]

    R. W. Engle, M. Kane, S. W. Tuholski. Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. Models of Working Memory: Mechanisms of Active Maintenance and Executive Control, A. Miyake, P. Shah, Eds., Cambridge, USA: Cambridge University Press, pp. 102–134, 1999.

    Google Scholar 

  16. [16]

    H. Olsson, L. Poom. Visual memory needs categories. Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 24, pp. 8776–8780, 2005.

    Article  Google Scholar 

  17. [17]

    A. Cervatiuc. Highly Proficient Adult Non-native English Speakers’ Perceptions of their Second Language Vocabulary Learning Process, Ph.D. dissertation, University of Calgary, Canada, 2007.

    Google Scholar 

  18. [18]

    P. Nation, R. Waring. Vocabulary size, text coverage and word lists. Vocabulary: Description, Acquisition and Pedagogy, N. Schmitt, M. McCarthy, Eds., Cambridge, USA: Cambridge University Press, pp. 6–19, 1997.

    Google Scholar 

  19. [19]

    G. A. Alvarez, P. Cavanagh. The capacity of visual shortterm memory is set both by visual information load and by number of objects. Psychological Science, vol. 15, no. 2, pp. 106–111, 2004.

    Article  Google Scholar 

  20. [20]

    S. J. Luck, E. K. Vogel. The capacity of visual working memory for features and conjunctions. Nature, vol. 390, no. 6657, pp. 279–281, 1997.

    Article  Google Scholar 

  21. [21]

    D. Nikolić, W. Singer. Creation of visual long-term memory. Perception & Psychophysics, vol. 69, no. 6, pp. 904–912, 2007.

    Article  Google Scholar 

  22. [22]

    E. Awh, J. Jonides. Overlapping mechanisms of attention and spatial working memory. Trends in Cognitive Sciences, vol. 5, no. 3, pp. 119–126, 2001.

    Article  Google Scholar 

  23. [23]

    J. S. Mayer, R. A. Bittner, D. Nikolić, C. Bledowski, R. Goebel, D. E. J. Linden. Common neural substrates for visual working memory and attention. Neuroimage, vol. 36, no. 2, pp. 441–453, 2007.

    Article  Google Scholar 

  24. [24]

    D. Nikolić. Testing the theory of practopoiesis using closed loops. Closed Loop Neuroscience, A. El Hady, Ed., Amsterdam: Academic Press, 2016.

    Google Scholar 

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The author would like to thank Hrvoje Nikolić, Raul C. Muresan, Shan Yu and Matt Mahoney for valuable comments on previous versions of the manuscript.

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Correspondence to Danko Nikolić.

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This work was supported by Hertie Foundation and Deutsche Forschungsgemeinschaft.

Recommended by Associate Editor Hong Qiao

Danko Nikolić received the degree in psychology and a degree in civil engineering from the University of Zagreb, Croatia. He received the Master-s degree and the Ph.D. degree in cognitive psychology from at the University of Oklahoma, USA. In 2010, he received a Private Docent title from the University of Zagreb, and in 2014 an associate professor title from the same university. He is now associated with Frankfurt Institute for Advanced Studies and works at DXC Technology in the field of artificial intelligence and data science.

He has a keen interest in addressing the explanatory gap between the brain and the mind. His interest is in how the physical world of neuronal activity produces the mental world of perception and cognition. For many years he headed an electrophysiological lab at Max-Planck Institute for Brain Research. He approached the problem of explanatory gap from both sides, bottom-up and top-down. The bottom-up approach begins from brain physiology. The top-down approach investigates the behavior and experiences. Each of the two approaches led him to develop a theory: The work on behavior and experiences led to the discovery of the phenomenon of ideasthesia (meaning “sensing concepts”). The work on physiology resulted in the theory of practopoiesis (meaning “creation of actions”).

He has conducted many empirical studies in the background of those theories. These studies involved simultaneous recordings of activity of 100+ neurons in the visual cortex (extracellular recordings), behavioral and imaging studies in visual cognition (attention, working memory, long-term memory), and empirical investigations of phenomenal experiences (synesthesia). His research was supported by grants from the Hertie Foundation, Deutsche Forschungsgemeinschaft (DFG) and other sources.

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Nikolić, D. Why deep neural nets cannot ever match biological intelligence and what to do about it?. Int. J. Autom. Comput. 14, 532–541 (2017). https://doi.org/10.1007/s11633-017-1093-8

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  • Artificial intelligence
  • neural networks
  • strong artificial intelligence
  • practopoiesis
  • machine learning